diff --git a/Cages/A.bib b/Cages/A.bib
index 1916fa5..52f33f9 100644
--- a/Cages/A.bib
+++ b/Cages/A.bib
@@ -10,6 +10,23 @@ @article{African.Buffalo
journal = {Computational Intelligence and Neuroscience}
}
+
+@article{Afterimage:.Artificial,
+ title = {Artificial Afterimage Algorithm: A New Bio-Inspired Metaheuristic Algorithm and Its Clustering Application},
+ volume = {15},
+ ISSN = {2076-3417},
+ url = {http://dx.doi.org/10.3390/app15031359},
+ DOI = {10.3390/app15031359},
+ number = {3},
+ journal = {Applied Sciences},
+ publisher = {MDPI AG},
+ author = {Demir, Murat},
+ year = {2025},
+ month = jan,
+ pages = {1359}
+}
+
+
@article{Armadillos,
title = {Giant Armadillo Optimization: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems},
volume = {8},
diff --git a/Cages/B.bib b/Cages/B.bib
index afa0588..43a95ac 100644
--- a/Cages/B.bib
+++ b/Cages/B.bib
@@ -80,6 +80,20 @@ @article{Bamboos
pages = {314}
}
+@article{Barnacles.Gooseneck,
+ title = {Novel gooseneck barnacle optimization a nature inspired technique for optimizing reactive power within systems of power with electric vehicles},
+ volume = {15},
+ ISSN = {2045-2322},
+ url = {http://dx.doi.org/10.1038/s41598-025-90178-x},
+ DOI = {10.1038/s41598-025-90178-x},
+ number = {1},
+ journal = {Scientific Reports},
+ publisher = {Springer Science and Business Media LLC},
+ author = {Zhuang, Juntao and Wang, Chengwei and Cheng, Qiong and Nourmohammadi, Samad and Alnowibet, Khalid A.},
+ year = {2025},
+ month = feb
+}
+
@incollection{Barnacles.Mating,
doi = {10.1007/978-981-13-3708-6_18},
url = {https://doi.org/10.1007/978-981-13-3708-6_18},
@@ -91,6 +105,20 @@ @incollection{Barnacles.Mating
booktitle = {Proceedings of the 10th National Technical Seminar on Underwater System Technology 2018}
}
+@article{Basketball.Team,
+ title = {Basketball team optimization algorithm (BTOA): a novel sport-inspired meta-heuristic optimizer for engineering applications},
+ volume = {15},
+ ISSN = {2045-2322},
+ url = {http://dx.doi.org/10.1038/s41598-025-05477-0},
+ DOI = {10.1038/s41598-025-05477-0},
+ number = {1},
+ journal = {Scientific Reports},
+ publisher = {Springer Science and Business Media LLC},
+ author = {Chen, Yujie and Wang, Guangyu and Yin, Baichuan and Ma, Chongyun and Wu, Zhiqiao and Gao, Ming},
+ year = {2025},
+ month = jul
+}
+
@incollection{Bats,
title={A new metaheuristic bat-inspired algorithm},
diff --git a/Cages/C.bib b/Cages/C.bib
index cce9588..ac8df52 100644
--- a/Cages/C.bib
+++ b/Cages/C.bib
@@ -281,6 +281,21 @@ @article{COVID19:.Distancing
journal = {{IEEE} Journal of Biomedical and Health Informatics}
}
+@article{Coyote.and.Badger,
+ title = {Coyote and Badger Optimization (CBO): A natural inspired meta-heuristic algorithm based on cooperative hunting},
+ volume = {140},
+ ISSN = {1007-5704},
+ url = {http://dx.doi.org/10.1016/j.cnsns.2024.108333},
+ DOI = {10.1016/j.cnsns.2024.108333},
+ journal = {Communications in Nonlinear Science and Numerical Simulation},
+ publisher = {Elsevier BV},
+ author = {Khatab, Mahmoud and El-Gamel, Mohamed and Saleh, Ahmed I. and El-Shenawy, Atallah and Rabie, Asmaa H.},
+ year = {2025},
+ month = jan,
+ pages = {108333}
+}
+
+
@inproceedings{Coyotes,
title={Coyote Optimization Algorithm: A New Metaheuristic for Global Optimization Problems},
author={Pierezan, Juliano and Coelho, Leandro Dos Santos},
@@ -366,3 +381,4 @@ @inproceedings{Cuckoos
title = {Cuckoo Search via L{\&}{\#}x00E9$\mathsemicolon$vy flights},
booktitle = {2009 World Congress on Nature {\&} Biologically Inspired Computing ({NaBIC})}
}
+
diff --git a/Cages/E.bib b/Cages/E.bib
index 1513f1f..6d55a4a 100644
--- a/Cages/E.bib
+++ b/Cages/E.bib
@@ -71,6 +71,23 @@ @inproceedings{Ecology
booktitle = {2011 Third World Congress on Nature and Biologically Inspired Computing}
}
+
+@article{Egyptian.Stray.Dogs,
+ title = {A Novel Optimization Algorithm Inspired by Egyptian Stray Dogs for Solving Multi-Objective Optimal Power Flow Problems},
+ volume = {7},
+ ISSN = {2571-5577},
+ url = {http://dx.doi.org/10.3390/asi7060122},
+ DOI = {10.3390/asi7060122},
+ number = {6},
+ journal = {Applied System Innovation},
+ publisher = {MDPI AG},
+ author = {ElMessmary, Mohamed H. and Diab, Hatem Y. and Abdelsalam, Mahmoud and Moussa, Mona F.},
+ year = {2024},
+ month = dec,
+ pages = {122}
+}
+
+
@article{Electromagnetism,
doi = {10.1016/j.ins.2010.12.024},
url = {https://doi.org/10.1016/j.ins.2010.12.024},
diff --git a/Cages/F.bib b/Cages/F.bib
index d25d72c..a239be5 100644
--- a/Cages/F.bib
+++ b/Cages/F.bib
@@ -236,3 +236,18 @@ @article{Fruit.Fly
title = {A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example},
journal = {Knowledge-Based Systems}
}
+
+
+@article{Fungal.Growth,
+ title = {Fungal growth optimizer: A novel nature-inspired metaheuristic algorithm for stochastic optimization},
+ volume = {437},
+ ISSN = {0045-7825},
+ url = {http://dx.doi.org/10.1016/j.cma.2025.117825},
+ DOI = {10.1016/j.cma.2025.117825},
+ journal = {Computer Methods in Applied Mechanics and Engineering},
+ publisher = {Elsevier BV},
+ author = {Abdel-Basset, Mohamed and Mohamed, Reda and Abouhawwash, Mohamed},
+ year = {2025},
+ month = mar,
+ pages = {117825}
+}
diff --git a/Cages/G.bib b/Cages/G.bib
index 453e7e0..ed7b441 100644
--- a/Cages/G.bib
+++ b/Cages/G.bib
@@ -115,6 +115,21 @@ @article{Glow.Worms
journal = {Swarm Intelligence}
}
+@article{Goat:.Foraging,
+ title = {Goat Optimization Algorithm: A Novel Bio-Inspired Metaheuristic for Global Optimization},
+ volume = {5},
+ ISSN = {2982-2882},
+ url = {http://dx.doi.org/10.63630/aiim.51.70},
+ DOI = {10.63630/aiim.51.70},
+ number = {1},
+ journal = {Applied Innovations in Industrial Management},
+ publisher = {Bio10},
+ author = {Nozari, Hamed and Abdi, Hoessein and Szmelter-Jarosz, Agnieszka},
+ year = {2025},
+ month = apr,
+ pages = {70–80}
+}
+
@article{Goats:.Wild.Goats.Climbing,
title = {Wild Goats Algorithm: An Evolutionary Algorithm to Solve the Real-World Optimization Problems},
volume = {14},
@@ -275,3 +290,18 @@ @article{Group.Decision-Making
title = {Collective decision optimization algorithm: A new heuristic optimization method},
journal = {Neurocomputing}
}
+
+@article{Groupers.and.Moray.Eels,
+ title = {Groupers and moray eels (GME) optimization: a nature-inspired metaheuristic algorithm for solving complex engineering problems},
+ volume = {37},
+ ISSN = {1433-3058},
+ url = {http://dx.doi.org/10.1007/s00521-024-10384-y},
+ DOI = {10.1007/s00521-024-10384-y},
+ number = {1},
+ journal = {Neural Computing and Applications},
+ publisher = {Springer Science and Business Media LLC},
+ author = {Mansour, Nehal A. and Saraya, M. Sabry and Saleh, Ahmed I.},
+ year = {2024},
+ month = nov,
+ pages = {63–90}
+}
diff --git a/Cages/H.bib b/Cages/H.bib
index aafa529..528e3bb 100644
--- a/Cages/H.bib
+++ b/Cages/H.bib
@@ -153,6 +153,21 @@ @article{Humans:.Cooking
month = sep
}
+@article{Humans:.Religion,
+ title = {Divine Religions Algorithm: a novel social-inspired metaheuristic algorithm for engineering and continuous optimization problems},
+ volume = {28},
+ ISSN = {1573-7543},
+ url = {http://dx.doi.org/10.1007/s10586-024-04954-x},
+ DOI = {10.1007/s10586-024-04954-x},
+ number = {4},
+ journal = {Cluster Computing},
+ publisher = {Springer Science and Business Media LLC},
+ author = {Mozhdehi, Ali Toufanzadeh and Khodadadi, Nima and Aboutalebi, Mohaddeseh and El-kenawy, El-Sayed M. and Hussien, Abdelazim G. and Zhao, Weiguo and Nadimi-Shahraki, Mohammad H. and Mirjalili, Seyedali},
+ year = {2025},
+ month = feb
+}
+
+
@article{Humans:.Reproduction,
title = {A novel Human Conception Optimizer for solving optimization problems},
volume = {12},
diff --git a/Cages/I.bib b/Cages/I.bib
index 5a95c76..9682c6b 100644
--- a/Cages/I.bib
+++ b/Cages/I.bib
@@ -12,6 +12,18 @@ @article{Invasive.Weeds
journal = {Ecological Informatics}
}
+@article{Invasive.Weeds:Seeds,
+ title = {Event-triggered dynamic seed invasive weed optimization (ET-DSIWO): a nature-inspired approach for non-stationary optimization},
+ ISSN = {1573-269X},
+ url = {http://dx.doi.org/10.1007/s11071-025-11513-5},
+ DOI = {10.1007/s11071-025-11513-5},
+ journal = {Nonlinear Dynamics},
+ publisher = {Springer Science and Business Media LLC},
+ author = {Jalaeian Farimani, Mohsen and Khalili Amirabadi, Roya and Esmaeili Ranjbar, Mohsen and Samadzadeh, Shima},
+ year = {2025},
+ month = jul
+}
+
@article{Interior.Design,
doi = {10.1016/j.isatra.2014.03.018},
url = {https://doi.org/10.1016/j.isatra.2014.03.018},
diff --git a/Cages/L.bib b/Cages/L.bib
index a37dd01..28c70ab 100644
--- a/Cages/L.bib
+++ b/Cages/L.bib
@@ -66,6 +66,21 @@ @article{Lions:.Swarm
pages = {105974}
}
+@article{Lionfish,
+ title = {Lionfish Search Algorithm: A Novel Nature‐Inspired Metaheuristic},
+ volume = {42},
+ ISSN = {1468-0394},
+ url = {http://dx.doi.org/10.1111/exsy.70016},
+ DOI = {10.1111/exsy.70016},
+ number = {4},
+ journal = {Expert Systems},
+ publisher = {Wiley},
+ author = {Kadhim, Saif Mohanad and Paw, Johnny Koh Siaw and Tak, Yaw Chong and Al‐Latief, Shahad Thamear Abd and Alkhayyat, Ahmed and Gupta, Deepak},
+ year = {2025},
+ month = mar
+}
+
+
@article{Lizards,
title = {Artificial lizard search optimization (ALSO): a novel nature-inspired meta-heuristic algorithm},
volume = {25},
diff --git a/Cages/O.bib b/Cages/O.bib
index 3607f4f..db08807 100644
--- a/Cages/O.bib
+++ b/Cages/O.bib
@@ -1,3 +1,18 @@
+
+@article{Oat:.Animated,
+ title = {The Animated Oat Optimization Algorithm: A nature-inspired metaheuristic for engineering optimization and a case study on Wireless Sensor Networks},
+ volume = {318},
+ ISSN = {0950-7051},
+ url = {http://dx.doi.org/10.1016/j.knosys.2025.113589},
+ DOI = {10.1016/j.knosys.2025.113589},
+ journal = {Knowledge-Based Systems},
+ publisher = {Elsevier BV},
+ author = {Wang, Ruo-Bin and Hu, Rui-Bin and Geng, Fang-Dong and Xu, Lin and Chu, Shu-Chuan and Pan, Jeng-Shyang and Meng, Zhen-Yu and Mirjalili, Seyedali},
+ year = {2025},
+ month = jun,
+ pages = {113589}
+}
+
@article{Optics,
doi = {10.1016/j.cor.2014.10.011},
url = {https://doi.org/10.1016/j.cor.2014.10.011},
@@ -11,6 +26,20 @@ @article{Optics
journal = {Computers {\&} Operations Research}
}
+@article{Orangutan,
+ title = {Orangutan Optimization Algorithm: An Innovative Bio-Inspired Metaheuristic Approach for Solving Engineering Optimization Problems},
+ volume = {18},
+ ISSN = {2185-3118},
+ url = {http://dx.doi.org/10.22266/ijies2025.0229.05},
+ DOI = {10.22266/ijies2025.0229.05},
+ number = {1},
+ journal = {International Journal of Intelligent Engineering and Systems},
+ publisher = {The Intelligent Networks and Systems Society},
+ year = {2025},
+ month = feb,
+ pages = {47–57}
+}
+
@article{Owls,
doi = {10.1016/j.tsep.2019.100431},
url = {https://doi.org/10.1016/j.tsep.2019.100431},
diff --git a/Cages/P.bib b/Cages/P.bib
index 8ed90ca..2491c7a 100644
--- a/Cages/P.bib
+++ b/Cages/P.bib
@@ -9,6 +9,20 @@ @inproceedings{Paddy.Fields
booktitle = {2009 International Conference on Industrial and Information Systems ({ICIIS})}
}
+@article{Pangolin:.Chinese,
+ title = {Chinese Pangolin Optimizer: a novel bio-inspired metaheuristic for solving optimization problems},
+ volume = {81},
+ ISSN = {1573-0484},
+ url = {http://dx.doi.org/10.1007/s11227-025-07004-4},
+ DOI = {10.1007/s11227-025-07004-4},
+ number = {4},
+ journal = {The Journal of Supercomputing},
+ publisher = {Springer Science and Business Media LLC},
+ author = {Guo, Zhiqing and Liu, Guangwei and Jiang, Feng},
+ year = {2025},
+ month = feb
+}
+
@incollection{Pearl.Hunting,
doi = {10.1007/978-3-642-34413-8_26},
url = {https://doi.org/10.1007/978-3-642-34413-8_26},
@@ -20,6 +34,50 @@ @incollection{Pearl.Hunting
booktitle = {Lecture Notes in Computer Science}
}
+@article{Perfumer,
+ title={Perfumer Optimization Algorithm: A Novel Human-Inspired Metaheuristic for Solving Optimization Tasks},
+ volume = {18},
+ ISSN = {2185-3118},
+ url = {http://dx.doi.org/10.22266/ijies2025.0531.41},
+ DOI = {10.22266/ijies2025.0531.41},
+ number = {4},
+ journal = {International Journal of Intelligent Engineering and Systems},
+ publisher = {The Intelligent Networks and Systems Society},
+ year = {2025},
+ month = may,
+ pages = {633–643}
+}
+
+@article{Phototropic.Growth,
+ title = {Phototropic growth algorithm: A novel metaheuristic inspired from phototropic growth of plants},
+ volume = {322},
+ ISSN = {0950-7051},
+ url = {http://dx.doi.org/10.1016/j.knosys.2025.113548},
+ DOI = {10.1016/j.knosys.2025.113548},
+ journal = {Knowledge-Based Systems},
+ publisher = {Elsevier BV},
+ author = {Bohat, Vijay Kumar and Hashim, Fatma A. and Batra, Harshit and Abd Elaziz, Mohamed},
+ year = {2025},
+ month = jul,
+ pages = {113548}
+}
+
+@article{Prairie.Dog,
+ title = {Prairie Dog Optimization Algorithm},
+ volume = {34},
+ ISSN = {1433-3058},
+ url = {http://dx.doi.org/10.1007/s00521-022-07530-9},
+ DOI = {10.1007/s00521-022-07530-9},
+ number = {22},
+ journal = {Neural Computing and Applications},
+ publisher = {Springer Science and Business Media LLC},
+ author = {Ezugwu, Absalom E. and Agushaka, Jeffrey O. and Abualigah, Laith and Mirjalili, Seyedali and Gandomi, Amir H.},
+ year = {2022},
+ month = jul,
+ pages = {20017–20065}
+}
+
+
@incollection{Penguins,
doi = {10.1007/978-3-642-38577-3_23},
url = {https://doi.org/10.1007/978-3-642-38577-3_23},
diff --git a/Cages/S.bib b/Cages/S.bib
index 7b10820..b78c92b 100644
--- a/Cages/S.bib
+++ b/Cages/S.bib
@@ -193,6 +193,22 @@ @incollection{Small.World
booktitle = {Lecture Notes in Computer Science}
}
+@article{Snake:.Spider-Tailed.Horned.Viper,
+ title={Spider-Tailed Horned Viper Optimization: An Effective Bio-Inspired Metaheuristic Algorithm for Solving Engineering Applications},
+ volume = {18},
+ ISSN = {2185-3118},
+ url = {http://dx.doi.org/10.22266/ijies2025.0229.03},
+ DOI = {10.22266/ijies2025.0229.03},
+ number = {1},
+ journal = {International Journal of Intelligent Engineering and Systems},
+ publisher = {The Intelligent Networks and Systems Society},
+ year = {2025},
+ month = feb,
+ pages = {25–35}
+}
+
+
+
@Article{Snakes,
title={Snake Optimizer: A novel meta-heuristic optimization algorithm},
author={Hashim, Fatma A and Hussien, Abdelazim G},
@@ -518,6 +534,22 @@ @InProceedings{Sonar
isbn="978-3-319-65172-9"
}
+@article{Starfish,
+ title = {Starfish optimization algorithm (SFOA): a bio-inspired metaheuristic algorithm for global optimization compared with 100 optimizers},
+ volume = {37},
+ ISSN = {1433-3058},
+ url = {http://dx.doi.org/10.1007/s00521-024-10694-1},
+ DOI = {10.1007/s00521-024-10694-1},
+ number = {5},
+ journal = {Neural Computing and Applications},
+ publisher = {Springer Science and Business Media LLC},
+ author = {Zhong, Changting and Li, Gang and Meng, Zeng and Li, Haijiang and Yildiz, Ali Riza and Mirjalili, Seyedali},
+ year = {2024},
+ month = dec,
+ pages = {3641–3683}
+}
+
+
@article{States.of.Matter,
doi = {10.1007/s11063-017-9750-z},
url = {https://doi.org/10.1007/s11063-017-9750-z},
@@ -532,6 +564,20 @@ @article{States.of.Matter
journal = {Neural Processing Letters}
}
+@article{Stellar.Oscillation,
+ title = {Stellar oscillation optimizer: a nature-inspired metaheuristic optimization algorithm},
+ volume = {28},
+ ISSN = {1573-7543},
+ url = {http://dx.doi.org/10.1007/s10586-024-04976-5},
+ DOI = {10.1007/s10586-024-04976-5},
+ number = {6},
+ journal = {Cluster Computing},
+ publisher = {Springer Science and Business Media LLC},
+ author = {Rodan, Ali and Al-Tamimi, Abdel-Karimi and Al-Alnemer, Loai and Mirjalili, Seyedali},
+ year = {2025},
+ month = jun
+}
+
@article{String.Theory,
title = {A new meta-heuristic optimization algorithm based on a paradigm from physics: string theory},
volume = {41},
diff --git a/Cages/T.bib b/Cages/T.bib
index 944b662..1577619 100644
--- a/Cages/T.bib
+++ b/Cages/T.bib
@@ -39,6 +39,23 @@ @article{Termites:.Alate
pages = {997–1017}
}
+
+@article{Thunderstorm.:Supercell,
+ title = {Supercell thunderstorm algorithm (STA): a nature-inspired metaheuristic algorithm for engineering optimization},
+ volume = {37},
+ ISSN = {1433-3058},
+ url = {http://dx.doi.org/10.1007/s00521-024-10848-1},
+ DOI = {10.1007/s00521-024-10848-1},
+ number = {10},
+ journal = {Neural Computing and Applications},
+ publisher = {Springer Science and Business Media LLC},
+ author = {Hassan, Mohamed H. and Kamel, Salah},
+ year = {2025},
+ month = feb,
+ pages = {7207–7260}
+}
+
+
@article{Time.Travel,
title = {A Particle Swarm Optimization Backtracking Technique Inspired by Science-Fiction Time Travel},
volume = {3},
@@ -54,6 +71,22 @@ @article{Time.Travel
pages = {390–415}
}
+
+@article{Tornado,
+ title = {Tornado optimizer with Coriolis force: a novel bio-inspired meta-heuristic algorithm for solving engineering problems},
+ volume = {58},
+ ISSN = {1573-7462},
+ url = {http://dx.doi.org/10.1007/s10462-025-11118-9},
+ DOI = {10.1007/s10462-025-11118-9},
+ number = {4},
+ journal = {Artificial Intelligence Review},
+ publisher = {Springer Science and Business Media LLC},
+ author = {Braik, Malik and Al-Hiary, Heba and Alzoubi, Hussein and Hammouri, Abdelaziz and Azmi Al-Betar, Mohammed and Awadallah, Mohammed A.},
+ year = {2025},
+ month = feb
+}
+
+
@article{Tree.Growth,
title = {Tree optimization algorithm (TOA): a novel metaheuristic approach for solving mathematical test functions and engineering problems},
volume = {16},
diff --git a/README.md b/README.md
index a7b1dca..d1f642f 100644
--- a/README.md
+++ b/README.md
@@ -1,8 +1,13 @@
# Evolutionary Computation Bestiary
[](https://zenodo.org/badge/latestdoi/54759561)
-Updated 2025-07-06
+Updated 2025-07-28
***
+---
+editor_options:
+ markdown:
+ wrap: 72
+---
> "Till now, madness has been thought a small island in an ocean of
> sanity. I am beginning to suspect that it is not an island at all but
@@ -88,489 +93,3 @@ bottom of the page on how to contribute!
### A
-- **African Buffalo**: Odili JB, Kahar MNM (2016). “Solving the Traveling Salesman's Problem Using the African Buffalo Optimization.” _Computational Intelligence and Neuroscience_, *2016*, 1-12. doi:[10.1155/2016/1510256](https://doi.org/10.1155/2016/1510256)
-- **Al-Biruni**: M. El-kenawy E, A. Abdelhamid A, Ibrahim A, Mirjalili S, Khodadad N, A. Al duailij M, Ali Alhussan A, Sami Khafaga D (2023). “Al-Biruni Earth Radius (BER) Metaheuristic Search Optimization Algorithm.” _Computer Systems Science and Engineering_, *45*(2), 1917–1934. ISSN 0267-6192, doi:[10.32604/csse.2023.032497](https://doi.org/10.32604/csse.2023.032497)
-- **Algae**: Uymaz SA, Tezel G, Yel E (2015). “Artificial algae algorithm (AAA) for nonlinear global optimization.” _Applied Soft Computing_, *31*, 153-171. doi:[10.1016/j.asoc.2015.03.003](https://doi.org/10.1016/j.asoc.2015.03.003)
-- **Ali Baba And The Forty Thieves**: Braik M, Ryalat MH, Al-Zoubi H (2021). “A novel meta-heuristic algorithm for solving numerical optimization problems: Ali Baba and the forty thieves.” _Neural Computing and Applications_, *34*(1), 409–455. ISSN 1433-3058, doi:[10.1007/s00521-021-06392-x](https://doi.org/10.1007/s00521-021-06392-x)
-- **American Zebras**: Mohapatra S, Mohapatra P (2023). “American zebra optimization algorithm for global optimization problems.” _Scientific Reports_, *13*(1). ISSN 2045-2322, doi:[10.1038/s41598-023-31876-2](https://doi.org/10.1038/s41598-023-31876-2)
-- **Amoeba**: Wang H, Lu X, Zhang X, Wang Q, Deng Y (2014). “A Bio-Inspired Method for the Constrained Shortest Path Problem.” _The Scientific World Journal_, *2014*, 1-11. doi:[10.1155/2014/271280](https://doi.org/10.1155/2014/271280)
-- **Amoeba: Plasmodium**: Zhu L, Kim S, Hara M, Aono M (2018). “Remarkable problem-solving ability of unicellular amoeboid organism and its mechanism.” _Royal Society Open Science_, *5*(12), 180396. doi:[10.1098/rsos.180396](https://doi.org/10.1098/rsos.180396)
-- **Anarchic Society**: Shayeghi H, Dadashpour J (2012). “Anarchic Society Optimization Based PID Control of an Automatic Voltage Regulator (AVR) System.” _Electrical and Electronic Engineering_, *2*(4), 199-207. doi:[10.5923/j.eee.20120204.05](https://doi.org/10.5923/j.eee.20120204.05)
-- **Andean Condors**: Almonacid B, Soto R (2018). “Andean Condor Algorithm for cell formation problems.” _Natural Computing_. doi:[10.1007/s11047-018-9675-0](https://doi.org/10.1007/s11047-018-9675-0)
-- **Anglerfish**: Pook MF, Ramlan EI (2018). “The Anglerfish algorithm: a derivation of randomized incremental construction technique for solving the traveling salesman problem.” _Evolutionary Intelligence_, *12*(1), 11-20. doi:[10.1007/s12065-018-0169-x](https://doi.org/10.1007/s12065-018-0169-x)
-- **Animal Behavior: Crow/Wolf Synergies**: Sassi M, Chelouah R (2023). “HHO-EAS: a new metaheuristic bio-inspired of the win–win hunting synergy between the two predators crow and wolf.” _Artificial Intelligence Review_, *56*(11), 12439–12504. ISSN 1573-7462, doi:[10.1007/s10462-023-10428-0](https://doi.org/10.1007/s10462-023-10428-0)
-- **Animal Behavior: Hunger Games**: Yang Y, Chen H, Heidari AA, Gandomi AH (2021). “Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts.” _Expert Systems with Applications_, *177*, 114864. ISSN 0957-4174, doi:[10.1016/j.eswa.2021.114864](https://doi.org/10.1016/j.eswa.2021.114864)
-- **Animal Behavior: Hunting**: Naderi B, Khalili M, Khamseh AA (2014). “Mathematical models and a hunting search algorithm for the no-wait flowshop scheduling with parallel machines.” _International Journal of Production Research_, *52*(9), 2667-2681. doi:[10.1080/00207543.2013.871389](https://doi.org/10.1080/00207543.2013.871389)
-- **Animal Behavior: Predation**: Tilahun SL, Ong HC (2015). “Prey-Predator Algorithm: A New Metaheuristic Algorithm for Optimization Problems.” _International Journal of Information Technology & Decision Making_, *14*(06), 1331-1352. doi:[10.1142/s021962201450031x](https://doi.org/10.1142/s021962201450031x)
-- **Animal Behavior: Searching**: He S, Wu Q, Saunders J (2009). “Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior.” _IEEE Transactions on Evolutionary Computation_, *13*(5), 973-990. doi:[10.1109/tevc.2009.2011992](https://doi.org/10.1109/tevc.2009.2011992)
-- **Ant Colony**: Maniezzo A (1992). “Distributed optimization by ant colonies.” In _Toward a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life_, 134. Mit Press.
-- **Ant Lion**: Mirjalili S (2015). “The Ant Lion Optimizer.” _Advances in Engineering Software_, *83*, 80-98. doi:[10.1016/j.advengsoft.2015.01.010](https://doi.org/10.1016/j.advengsoft.2015.01.010)
-- **Antibodies**: De Castro LN, Von Zuben FJ (2000). “The clonal selection algorithm with engineering applications.” In _Proceedings of GECCO_, volume 2000, 36-39.
-- **Aquilas**: Abualigah L, Yousri D, Abd Elaziz M, Ewees AA, Al-qaness MA, Gandomi AH (2021). “Aquila Optimizer: A novel meta-heuristic optimization algorithm.” _Computers & Industrial Engineering_, *157*, 107250. ISSN 0360-8352, doi:[10.1016/j.cie.2021.107250](https://doi.org/10.1016/j.cie.2021.107250)
-- **Armadillos**: Alsayyed O, Hamadneh T, Al-Tarawneh H, Alqudah M, Gochhait S, Leonova I, Malik OP, Dehghani M (2023). “Giant Armadillo Optimization: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems.” _Biomimetics_, *8*(8), 619. ISSN 2313-7673, doi:[10.3390/biomimetics8080619](https://doi.org/10.3390/biomimetics8080619)
-- **Artillery**: Pijarski P, Kacejko P (2019). “A new metaheuristic optimization method: the algorithm of the innovative gunner (AIG).” _Engineering Optimization_, *51*(12), 2049-2068. doi:[10.1080/0305215x.2019.1565282](https://doi.org/10.1080/0305215x.2019.1565282)
-- **Axolotls**: Villuendas-Rey Y, Velázquez-Rodríguez JL, Alanis-Tamez MD, Moreno-Ibarra M, Yáñez-Márquez C (2021). “Mexican Axolotl Optimization: A Novel Bioinspired Heuristic.” _Mathematics_, *9*(7), 781. ISSN 2227-7390, doi:[10.3390/math9070781](https://doi.org/10.3390/math9070781)
-
-### B
-- **Bachelors**: Hu TC, Kahng AB, Tsao CA (1995). “Old Bachelor Acceptance: A New Class of Non-Monotone Threshold Accepting Methods.” _ORSA Journal on Computing_, *7*(4), 417-425. doi:[10.1287/ijoc.7.4.417](https://doi.org/10.1287/ijoc.7.4.417)
-- **Bacteria: Bacterial Chemotaxis**: Muller S, Marchetto J, Airaghi S, Kournoutsakos P (2002). “Optimization based on bacterial chemotaxis.” _IEEE Transactions on Evolutionary Computation_, *6*(1), 16-29. doi:[10.1109/4235.985689](https://doi.org/10.1109/4235.985689)
-- **Bacteria: Bacterial Foraging**: Passino K (2002). “Biomimicry of bacterial foraging for distributed optimization and control.” _IEEE Control Systems Magazine_, *22*(3), 52-67. doi:[10.1109/mcs.2002.1004010](https://doi.org/10.1109/mcs.2002.1004010)
-- **Bacteria: Bacterial Swarming**: Chu Y, Mi H, Liao H, Ji Z, Wu QH (2008). “A Fast Bacterial Swarming Algorithm for high-dimensional function optimization.” In _2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)_. doi:[10.1109/cec.2008.4631222](https://doi.org/10.1109/cec.2008.4631222)
-- **Bacteria: Magnetotactic Bacteria**: Mo H, Xu L (2013). “Magnetotactic bacteria optimization algorithm for multimodal optimization.” In _2013 IEEE Symposium on Swarm Intelligence (SIS)_. doi:[10.1109/sis.2013.6615185](https://doi.org/10.1109/sis.2013.6615185)
-- **Bamboos**: Pan J, Yue L, Chu S, Hu P, Yan B, Yang H (2023). “Binary Bamboo Forest Growth Optimization Algorithm for Feature Selection Problem.” _Entropy_, *25*(2), 314. ISSN 1099-4300, doi:[10.3390/e25020314](https://doi.org/10.3390/e25020314)
-- **Barnacles Mating**: Sulaiman MH, Mustaffa Z, Saari MM, Daniyal H, Mohamad AJ, Othman MR, Mohamed MR (2019). “Barnacles Mating Optimizer Algorithm for Optimization.” In _Proceedings of the 10th National Technical Seminar on Underwater System Technology 2018_, 211-218. Springer Singapore. doi:[10.1007/978-981-13-3708-6_18](https://doi.org/10.1007/978-981-13-3708-6_18)
-- **Bats**: Yang X (2010). “A new metaheuristic bat-inspired algorithm.” In _Nature inspired cooperative strategies for optimization (NICSO 2010)_, 65-74. Springer.
-- **Battle Royale Game**: Farshi TR (2020). “Battle royale optimization algorithm.” _Neural Computing and Applications_, *33*(4), 1139-1157. doi:[10.1007/s00521-020-05004-4](https://doi.org/10.1007/s00521-020-05004-4)
-- **Beans: Seeds**: Feng T, Xie Q, Hu H, Song L, Cui C, Zhang X (2015). “Bean Optimization Algorithm Based on Negative Binomial Distribution.” In _Lecture Notes in Computer Science_, 82–88. ISBN 9783319204666, doi:[10.1007/978-3-319-20466-6_9](https://doi.org/10.1007/978-3-319-20466-6_9)
-- **Beans: Transmission**: Zhang X, Sun B, Mei T, Wang R (2010). “Post-disaster restoration based on fuzzy preference relation and Bean Optimization Algorithm.” In _2010 IEEE Youth Conference on Information, Computing and Telecommunications_. doi:[10.1109/ycict.2010.5713097](https://doi.org/10.1109/ycict.2010.5713097)
-- **Bees: Bee Colonies**: Teodorovic D, Lucic P, Markovic G, Orco MD (2006). “Bee Colony Optimization: Principles and Applications.” In _2006 8th Seminar on Neural Network Applications in Electrical Engineering_. doi:[10.1109/neurel.2006.341200](https://doi.org/10.1109/neurel.2006.341200)
-- **Bees: Bee Colonies 2**: Karaboga D, Basturk B (2007). “Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems.” In _Lecture Notes in Computer Science_, 789-798. Springer Berlin Heidelberg. doi:[10.1007/978-3-540-72950-1_77](https://doi.org/10.1007/978-3-540-72950-1_77)
-- **Bees: Bumblebees**: Comellas F, Martinez-Navarro J (2009). “Bumblebees.” In _Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation - GEC \textquotesingle09_. doi:[10.1145/1543834.1543949](https://doi.org/10.1145/1543834.1543949)
-- **Bees: Honey Bee Marriages**: Abbass H (2001). “MBO: marriage in honey bees optimization-a Haplometrosis polygynous swarming approach.” In _Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546)_. doi:[10.1109/cec.2001.934391](https://doi.org/10.1109/cec.2001.934391)
-- **Bees: Queen Bees**: Jung SH (2003). “Queen-bee evolution for genetic algorithms.” _Electronics Letters_, *39*(6), 575. doi:[10.1049/el:20030383](https://doi.org/10.1049/el:20030383)
-- **Beetles: Dark Beetles**: Kallioras NA, Lagaros ND, Avtzis DN (2018). “Pity beetle algorithm — A new metaheuristic inspired by the behavior of bark beetles.” _Advances in Engineering Software_, *121*, 147-166. doi:[10.1016/j.advengsoft.2018.04.007](https://doi.org/10.1016/j.advengsoft.2018.04.007)
-- **Beetles: Longicorn Beetles**: Han X, Du X, Yu P (2020). “ATLA: A novel metaheuristic optimization algorithm inspired by the mating search behavior of longicorn beetles in the nature.” _IOP Conference Series: Materials Science and Engineering_, *782*(5), 052028. ISSN 1757-899X, doi:[10.1088/1757-899x/782/5/052028](https://doi.org/10.1088/1757-899x/782/5/052028)
-- **Big Bang**: Erol OK, Eksin I (2006). “A new optimization method: Big Bang—Big Crunch.” _Advances in Engineering Software_, *37*(2), 106-111. doi:[10.1016/j.advengsoft.2005.04.005](https://doi.org/10.1016/j.advengsoft.2005.04.005)
-- **Biogeography**: Simon D (2008). “Biogeography-Based Optimization.” _IEEE Transactions on Evolutionary Computation_, *12*(6), 702-713. doi:[10.1109/tevc.2008.919004](https://doi.org/10.1109/tevc.2008.919004)
-- **Birds: Bird Migrations**: Duman E, Uysal M, Alkaya AF (2012). “Migrating Birds Optimization: A new metaheuristic approach and its performance on quadratic assignment problem.” _Information Sciences_, *217*, 65-77. doi:[10.1016/j.ins.2012.06.032](https://doi.org/10.1016/j.ins.2012.06.032)
-- **Birds: Birds Mating**: Askarzadeh A (2014). “Bird mating optimizer: An optimization algorithm inspired by bird mating strategies.” _Communications in Nonlinear Science and Numerical Simulation_, *19*(4), 1213-1228. doi:[10.1016/j.cnsns.2013.08.027](https://doi.org/10.1016/j.cnsns.2013.08.027)
-- **Birds: Escaping Strategies**: Shahrouzi M, Kaveh A (2022). “An efficient derivative-free optimization algorithm inspired by avian life-saving manoeuvres.” _Journal of Computational Science_, *57*, 101483. ISSN 1877-7503, doi:[10.1016/j.jocs.2021.101483](https://doi.org/10.1016/j.jocs.2021.101483)
-- **Birds: Hitchcock Birds**: Morais RG, Nedjah N, Mourelle LM (2019). “A novel metaheuristic inspired by Hitchcock birds' behavior for efficient optimization of large search spaces of high dimensionality.” _Soft Computing_, *24*(8), 5633-5655. doi:[10.1007/s00500-019-04102-3](https://doi.org/10.1007/s00500-019-04102-3)
-- **Bison**: Kazikova A, Pluhacek M, Senkerik R, Viktorin A (2018). “Proposal of a New Swarm Optimization Method Inspired in Bison Behavior.” In _Recent Advances in Soft Computing_, 146-156. Springer International Publishing. doi:[10.1007/978-3-319-97888-8_13](https://doi.org/10.1007/978-3-319-97888-8_13)
-- **Black Holes**: Hatamlou A (2013). “Black hole: A new heuristic optimization approach for data clustering.” _Information Sciences_, *222*, 175-184. doi:[10.1016/j.ins.2012.08.023](https://doi.org/10.1016/j.ins.2012.08.023)
-- **Black Widow**: Hayyolalam V, Kazem AAP (2020). “Black Widow Optimization Algorithm: A novel meta-heuristic approach for solving engineering optimization problems.” _Engineering Applications of Artificial Intelligence_, *87*, 103249. doi:[10.1016/j.engappai.2019.103249](https://doi.org/10.1016/j.engappai.2019.103249)
-- **Bonobos**: Das AK, Nikum AK, Krishnan SV, Pratihar DK (2020). “Multi-objective Bonobo Optimizer (MOBO): an intelligent heuristic for multi-criteria optimization.” _Knowledge and Information Systems_, *62*(11), 4407-4444. doi:[10.1007/s10115-020-01503-x](https://doi.org/10.1007/s10115-020-01503-x)
-- **Brainstorming**: Shi Y (2011). “An Optimization Algorithm Based on Brainstorming Process.” _International Journal of Swarm Intelligence Research_, *2*(4), 35-62. doi:[10.4018/ijsir.2011100103](https://doi.org/10.4018/ijsir.2011100103)
-- **BrunsVigia Flower**: Ghaemidizaji M, Dadkhah C, Leung H (2018). “A New Optimization Algorithm Based on the Behavior of BrunsVigia Flower.” In _2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)_. doi:[10.1109/smc.2018.00055](https://doi.org/10.1109/smc.2018.00055)
-- **Buses**: Bodaghi M, Samieefar K (2018). “Meta-heuristic bus transportation algorithm.” _Iran Journal of Computer Science_. doi:[10.1007/s42044-018-0025-2](https://doi.org/10.1007/s42044-018-0025-2)
-- **Butterflies: Monarch Butterflies**: Wang G, Deb S, Cui Z (2015). “Monarch butterfly optimization.” _Neural Computing and Applications_. doi:[10.1007/s00521-015-1923-y](https://doi.org/10.1007/s00521-015-1923-y)
-- **Butterflies: Regular Butterflies**: Arora S, Singh S (2018). “Butterfly optimization algorithm: a novel approach for global optimization.” _Soft Computing_. doi:[10.1007/s00500-018-3102-4](https://doi.org/10.1007/s00500-018-3102-4)
-- **Buzzards**: Arshaghi A, Ashourian M, Ghabeli L (2019). “Buzzard Optimization Algorithm: A Nature-Inspired Metaheuristic Algorithm.” _Majlesi Journal of Electrical Engineering_, *13*(3), 83-98. .
-
-### C
-- **Camels**: M. K. Ibrahim RSA (2016). “Novel Optimization Algorithm Inspired by Camel Traveling Behavior.” _Iraq J. Electrical and Electronic Engineering_, *12*(2), 167-177. ISSN 18145892,
-- **Cancers**: Tang D, Dong S, Jiang Y, Li H, Huang Y (2015). “ITGO: Invasive tumor growth optimization algorithm.” _Applied Soft Computing_, *36*, 670-698. doi:[10.1016/j.asoc.2015.07.045](https://doi.org/10.1016/j.asoc.2015.07.045)
-- **Cats: Behaviors**: Chu S, Tsai P, Pan J (2006). “Cat Swarm Optimization.” In _Lecture Notes in Computer Science_, 854-858. Springer Berlin Heidelberg. doi:[10.1007/978-3-540-36668-3_94](https://doi.org/10.1007/978-3-540-36668-3_94)
-- **Cats: Sand Cats**: Seyyedabbasi A, Kiani F (2022). “Sand Cat swarm optimization: a nature-inspired algorithm to solve global optimization problems.” _Engineering with Computers_, *39*(4), 2627–2651. ISSN 1435-5663, doi:[10.1007/s00366-022-01604-x](https://doi.org/10.1007/s00366-022-01604-x)
-- **Central Force**: Formato RA (2007). “CENTRAL FORCE OPTIMIZATION: A NEW METAHEURISTIC WITH APPLICATIONS IN APPLIED ELECTROMAGNETICS.” _Progress In Electromagnetics Research_, *77*, 425-491. doi:[10.2528/pier07082403](https://doi.org/10.2528/pier07082403)
-- **Chameleons**: Braik MS (2021). “Chameleon Swarm Algorithm: A bio-inspired optimizer for solving engineering design problems.” _Expert Systems with Applications_, *174*, 114685. doi:[10.1016/j.eswa.2021.114685](https://doi.org/10.1016/j.eswa.2021.114685)
-- **Charged Systems**: Kaveh A, Talatahari S (2010). “A novel heuristic optimization method: charged system search.” _Acta Mechanica_, *213*(3-4), 267-289. doi:[10.1007/s00707-009-0270-4](https://doi.org/10.1007/s00707-009-0270-4)
-- **Cheetah**: Klein CE, Mariani V, dos Santos Coelho L (2018). “Cheetah Based Optimization Algorithm: A Novel Swarm Intelligence Paradigm.” In _Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning_.
-- **Chemical Reactions**: Alatas B (2011). “ACROA: Artificial Chemical Reaction Optimization Algorithm for global optimization.” _Expert Systems with Applications_, *38*(10), 13170-13180. doi:[10.1016/j.eswa.2011.04.126](https://doi.org/10.1016/j.eswa.2011.04.126)
-- **Chickens: Chicken Laying Eggs**: Hosseini E (2017). “Laying Chicken Algorithm: A New Meta-Heuristic Approach to Solve Continuous Programming Problems.” _Journal of Applied & Computational Mathematics_, *06*(01). doi:[10.4172/2168-9679.1000344](https://doi.org/10.4172/2168-9679.1000344)
-- **Chickens: Chicken Swarms**: Meng X, Liu Y, Gao X, Zhang H (2014). “A New Bio-inspired Algorithm: Chicken Swarm Optimization.” In _Lecture Notes in Computer Science_, 86-94. Springer International Publishing. doi:[10.1007/978-3-319-11857-4_10](https://doi.org/10.1007/978-3-319-11857-4_10)
-- **Children's Drawings**: Ameen AA, Rashid TA, Askar S (2023). “CDDO–HS: Child Drawing Development Optimization–Harmony Search Algorithm.” _Applied Sciences_, *13*(9), 5795. ISSN 2076-3417, doi:[10.3390/app13095795](https://doi.org/10.3390/app13095795)
-- **Chimps**: Khishe M, Mosavi M (2020). “Chimp optimization algorithm.” _Expert Systems with Applications_, *149*, 113338. ISSN 0957-4174, doi:[10.1016/j.eswa.2020.113338](https://doi.org/10.1016/j.eswa.2020.113338)
-- **Clouds**: YAN G, HAO Z (2013). “A NOVEL OPTIMIZATION ALGORITHM BASED ON ATMOSPHERE CLOUDS MODEL.” _International Journal of Computational Intelligence and Applications_, *12*(01), 1350002. doi:[10.1142/s1469026813500028](https://doi.org/10.1142/s1469026813500028)
-- **Cockroaches**: Obagbuwa IC, Adewumi AO (2014). “An Improved Cockroach Swarm Optimization.” _The Scientific World Journal_, *2014*, 1-13. doi:[10.1155/2014/375358](https://doi.org/10.1155/2014/375358)
-- **Colliding Bodies**: Kaveh A, Mahdavi V (2014). “Colliding bodies optimization: A novel meta-heuristic method.” _Computers & Structures_, *139*, 18-27. doi:[10.1016/j.compstruc.2014.04.005](https://doi.org/10.1016/j.compstruc.2014.04.005)
-- **Community of scientists**: Alfredo M, Valentino S (2012). “Community of scientist optimization: An autonomy oriented approach to distributed optimization.” _AI Communications_, *25*(2), 157–172. ISSN 0921-7126, doi:[10.3233/AIC-2012-0526](https://doi.org/10.3233/AIC-2012-0526)
-- **Consultants**: Iordache S (2010). “Consultant-guided search.” In _Proceedings of the 12th annual conference on Genetic and evolutionary computation - GECCO \textquotesingle10_. doi:[10.1145/1830483.1830526](https://doi.org/10.1145/1830483.1830526)
-- **Coral Reefs**: Salcedo-Sanz S, Ser JD, Landa-Torres I, Gil-López S, Portilla-Figueras JA (2014). “The Coral Reefs Optimization Algorithm: A Novel Metaheuristic for Efficiently Solving Optimization Problems.” _The Scientific World Journal_, *2014*, 1-15. doi:[10.1155/2014/739768](https://doi.org/10.1155/2014/739768)
-- **COVID19: Containment**: Emami H (2022). “Anti-coronavirus optimization algorithm.” _Soft Computing_, *26*(11), 4991–5023. ISSN 1433-7479, doi:[10.1007/s00500-022-06903-5](https://doi.org/10.1007/s00500-022-06903-5)
-- **COVID19: Distancing**: Hosseini E, Ghafoor KZ, Sadiq AS, Guizani M, Emrouznejad A (2020). “COVID-19 Optimizer Algorithm, Modeling and Controlling of Coronavirus Distribution Process.” _IEEE Journal of Biomedical and Health Informatics_, *24*(10), 2765-2775. doi:[10.1109/jbhi.2020.3012487](https://doi.org/10.1109/jbhi.2020.3012487)
-- **COVID19: Propagation**: Martínez-Álvarez F, Asencio-Cortés G, Torres JF, Gutiérrez-Avilés D, Melgar-García L, Pérez-Chacón R, Rubio-Escudero C, Riquelme JC, Troncoso A (2020). “Coronavirus Optimization Algorithm: A Bioinspired Metaheuristic Based on the COVID-19 Propagation Model.” _Big Data_, *8*(4), 308–322. ISSN 2167-647X, doi:[10.1089/big.2020.0051](https://doi.org/10.1089/big.2020.0051)
-- **Coyotes**: Pierezan J, Coelho LDS (2018). “Coyote Optimization Algorithm: A New Metaheuristic for Global Optimization Problems.” In _2018 IEEE Congress on Evolutionary Computation (CEC)_, 1-8. IEEE.
-- **Crab**: Chifu VR, Salomie I, Chifu ES, Negrean A, Jeflea H, Antal M (2014). “Crab mating optimization algorithm.” In _2014 18th International Conference on System Theory, Control and Computing (ICSTCC)_. doi:[10.1109/icstcc.2014.6982441](https://doi.org/10.1109/icstcc.2014.6982441)
-- **Crickets**: Canayaz M, Karci A (2015). “Cricket behaviour-based evolutionary computation technique in solving engineering optimization problems.” _Applied Intelligence_, *44*(2), 362–376. ISSN 1573-7497, doi:[10.1007/s10489-015-0706-6](https://doi.org/10.1007/s10489-015-0706-6)
-- **Crows**: Askarzadeh A (2016). “A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm.” _Computers & Structures_, *169*, 1-12. doi:[10.1016/j.compstruc.2016.03.001](https://doi.org/10.1016/j.compstruc.2016.03.001)
-- **Crows: Chaotic**: Rizk-Allah RM, Hassanien AE, Bhattacharyya S (2018). “Chaotic crow search algorithm for fractional optimization problems.” _Applied Soft Computing_, *71*, 1161-1175. doi:[10.1016/j.asoc.2018.03.019](https://doi.org/10.1016/j.asoc.2018.03.019)
-- **Crystal Energy**: Feng X, Ma M, Yu H (2014). “Crystal Energy Optimization Algorithm.” _Computational Intelligence_, *32*(2), 284-322. doi:[10.1111/coin.12053](https://doi.org/10.1111/coin.12053)
-- **Cuckoos**: Yang X, Deb S (2009). “Cuckoo Search via L&\#x00E9$\mathsemicolon$vy flights.” In _2009 World Congress on Nature & Biologically Inspired Computing (NaBIC)_. doi:[10.1109/nabic.2009.5393690](https://doi.org/10.1109/nabic.2009.5393690)
-
-### D
-- **Dandelions**: Zhao S, Zhang T, Ma S, Chen M (2022). “Dandelion Optimizer: A nature-inspired metaheuristic algorithm for engineering applications.” _Engineering Applications of Artificial Intelligence_, *114*, 105075. ISSN 0952-1976, doi:[10.1016/j.engappai.2022.105075](https://doi.org/10.1016/j.engappai.2022.105075)
-- **Deer: Scottish Red Deer**: Fard AF, Hajiaghaei-Keshteli M (2016). “Red Deer Algorithm (RDA); A New Optimization Algorithm Inspired by Red Deers’ Mating.” In _International Conference on Industrial Engineering, IEEE.,(2016 e)_, 33-34.
-- **Dendritic Cells**: Greensmith J, Aickelin U, Cayzer S (2005). “Introducing dendritic cells as a novel immune-inspired algorithm for anomaly detection.” In _International Conference on Artificial Immune Systems_, 153-167. Springer.
-- **Dice Games**: DEHGHANI M, MONTAZERI Z, MALIK OP (2019). “DGO: Dice Game Optimizer.” _GAZI UNIVERSITY JOURNAL OF SCIENCE_, *32*(3), 871-882. doi:[10.35378/gujs.484643](https://doi.org/10.35378/gujs.484643)
-- **Dogs: African Wild Dogs**: Subramanian C, Sekar A, Subramanian K (2013). “A New Engineering Optimization Method: African Wild Dog Algorithm.” _International Journal of Soft Computing_, *8*(3).
-- **Dogs: Australian Dingo Dogs**: Peraza-Vázquez H, Peña-Delgado AF, Echavarría-Castillo G, Morales-Cepeda AB, Velasco-Álvarez J, Ruiz-Perez F (2021). “A Bio-Inspired Method for Engineering Design Optimization Inspired by Dingoes Hunting Strategies.” _Mathematical Problems in Engineering_, *2021*, 1–19. ISSN 1024-123X, doi:[10.1155/2021/9107547](https://doi.org/10.1155/2021/9107547)
-- **Dogs: Border Collie**: Dutta T, Bhattacharyya S, Dey S, Platos J (2020). “Border Collie Optimization.” _IEEE Access_, *8*, 109177-109197. doi:[10.1109/access.2020.2999540](https://doi.org/10.1109/access.2020.2999540)
-- **Dolphins: Dolphin Echolocation**: Kaveh A, Farhoudi N (2013). “A new optimization method: Dolphin echolocation.” _Advances in Engineering Software_, *59*, 53-70. doi:[10.1016/j.advengsoft.2013.03.004](https://doi.org/10.1016/j.advengsoft.2013.03.004)
-- **Dolphins: Dolphin Partners**: Shiqin Y, Jianjun J, Guangxing Y (2009). “A Dolphin Partner Optimization.” In _2009 WRI Global Congress on Intelligent Systems_. doi:[10.1109/gcis.2009.464](https://doi.org/10.1109/gcis.2009.464)
-- **Dolphins: Dolphin Swarms**: Wu T, Yao M, Yang J (2016). “Dolphin swarm algorithm.” _Frontiers of Information Technology & Electronic Engineering_, *17*(8), 717–729. ISSN 2095-9230, doi:[10.1631/fitee.1500287](https://doi.org/10.1631/fitee.1500287)
-- **Donkeys**: Dehghani M, Mardaneh M, Malik OP, NouraeiPour SM (2019). “DTO: Donkey Theorem Optimization.” In _2019 27th Iranian Conference on Electrical Engineering (ICEE)_. doi:[10.1109/iraniancee.2019.8786601](https://doi.org/10.1109/iraniancee.2019.8786601)
-- **Dragonflies**: Mirjalili S (2015). “Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems.” _Neural Computing and Applications_, *27*(4), 1053-1073. doi:[10.1007/s00521-015-1920-1](https://doi.org/10.1007/s00521-015-1920-1)
-- **Duelists**: Biyanto TR, Fibrianto HY, Nugroho G, Hatta AM, Listijorini E, Budiati T, Huda H (2016). “Duelist Algorithm: An Algorithm Inspired by How Duelist Improve Their Capabilities in a Duel.” In Tan Y, Shi Y, Niu B (eds.), _Advances in Swarm Intelligence_, 39-47. ISBN 978-3-319-41000-5.
-- **Dwarf Mongooses**: Agushaka JO, Ezugwu AE, Abualigah L (2022). “Dwarf Mongoose Optimization Algorithm.” _Computer Methods in Applied Mechanics and Engineering_, *391*, 114570. ISSN 0045-7825, doi:[10.1016/j.cma.2022.114570](https://doi.org/10.1016/j.cma.2022.114570)
-
-### E
-- **Eagles: Bald Eagles**: Alsattar HA, Zaidan AA, Zaidan BB (2019). “Novel meta-heuristic bald eagle search optimisation algorithm.” _Artificial Intelligence Review_, *53*(3), 2237–2264. ISSN 1573-7462, doi:[10.1007/s10462-019-09732-5](https://doi.org/10.1007/s10462-019-09732-5)
-- **Eagles: Golden Eagles**: Mohammadi-Balani A, Dehghan Nayeri M, Azar A, Taghizadeh-Yazdi M (2021). “Golden eagle optimizer: A nature-inspired metaheuristic algorithm.” _Computers & Industrial Engineering_, *152*, 107050. ISSN 0360-8352, doi:[10.1016/j.cie.2020.107050](https://doi.org/10.1016/j.cie.2020.107050)
-- **Eagles: Lévy Walk**: Yang X, Deb S (2010). “Eagle Strategy Using Lévy Walk and Firefly Algorithms for Stochastic Optimization.” In _Nature Inspired Cooperative Strategies for Optimization (NICSO 2010)_, 101-111. Springer Berlin Heidelberg. doi:[10.1007/978-3-642-12538-6_9](https://doi.org/10.1007/978-3-642-12538-6_9)
-- **Earthworms**: Wang G, Deb S, Coelho LDS (2015). “Earthworm optimization algorithm: a bio-inspired metaheuristic algorithm for global optimization problems.” _International Journal of Bio-Inspired Computation_, *7*, 1-23.
-- **Ebola**: Oyelade ON, Ezugwu AE, Mohamed TIA, Abualigah L (2022). “Ebola Optimization Search Algorithm: A New Nature-Inspired Metaheuristic Optimization Algorithm.” _IEEE Access_, *10*, 16150–16177. ISSN 2169-3536, doi:[10.1109/access.2022.3147821](https://doi.org/10.1109/access.2022.3147821)
-- **Ecogeography**: Zheng Y, Ling H, Xue J (2014). “Ecogeography-based optimization: Enhancing biogeography-based optimization with ecogeographic barriers and differentiations.” _Computers & Operations Research_, *50*, 115-127. doi:[10.1016/j.cor.2014.04.013](https://doi.org/10.1016/j.cor.2014.04.013)
-- **Ecology**: Parpinelli RS, Lopes HS (2011). “An eco-inspired evolutionary algorithm applied to numerical optimization.” In _2011 Third World Congress on Nature and Biologically Inspired Computing_. doi:[10.1109/nabic.2011.6089631](https://doi.org/10.1109/nabic.2011.6089631)
-- **Electromagnetism**: Cuevas E, Oliva D, Zaldivar D, Pérez-Cisneros M, Sossa H (2012). “Circle detection using electro-magnetism optimization.” _Information Sciences_, *182*(1), 40-55. doi:[10.1016/j.ins.2010.12.024](https://doi.org/10.1016/j.ins.2010.12.024)
-- **Electrons: Flow**: Khalafallah A, Abdel-Raheem M (2011). “Electimize: New Evolutionary Algorithm for Optimization with Application in Construction Engineering.” _Journal of Computing in Civil Engineering_, *25*(3), 192-201. doi:10.1061/(asce)cp.1943-5487.0000080
-- **Electrons: Radar**: Rahmanzadeh S, Pishvaee MS (2019). “Electron radar search algorithm: a novel developed meta-heuristic algorithm.” _Soft Computing_, *24*(11), 8443-8465. doi:[10.1007/s00500-019-04410-8](https://doi.org/10.1007/s00500-019-04410-8)
-- **Elephants: Elephant Clans**: Jafari M, Salajegheh E, Salajegheh J (2021). “Elephant clan optimization: A nature-inspired metaheuristic algorithm for the optimal design of structures.” _Applied Soft Computing_, *113*, 107892. ISSN 1568-4946, doi:[10.1016/j.asoc.2021.107892](https://doi.org/10.1016/j.asoc.2021.107892)
-- **Elephants: Elephant Herds**: Wang G, Deb S, dos S. Coelho L (2015). “Elephant Herding Optimization.” In _2015 3rd International Symposium on Computational and Business Intelligence (ISCBI)_. doi:[10.1109/iscbi.2015.8](https://doi.org/10.1109/iscbi.2015.8)
-- **Elephants: Regular Elephants**: Deb S, Fong S, Tian Z (2015). “Elephant Search Algorithm for optimization problems.” In _2015 Tenth International Conference on Digital Information Management (ICDIM)_. doi:[10.1109/icdim.2015.7381893](https://doi.org/10.1109/icdim.2015.7381893)
-- **Emotions**: Xu Y, Cui Z, Zeng J (2010). “Social Emotional Optimization Algorithm for Nonlinear Constrained Optimization Problems.” In _Swarm, Evolutionary, and Memetic Computing_, 583-590. Springer Berlin Heidelberg. doi:[10.1007/978-3-642-17563-3_68](https://doi.org/10.1007/978-3-642-17563-3_68)
-- **Epidemics**: Huang G (2016). “Artificial infectious disease optimization: A SEIQR epidemic dynamic model-based function optimization~algorithm.” _Swarm and Evolutionary Computation_, *27*, 31-67. doi:[10.1016/j.swevo.2015.09.007](https://doi.org/10.1016/j.swevo.2015.09.007)
-- **Experts**: Melo VVD (2014). “Kaizen programming.” In _Proceedings of the 2014 conference on Genetic and evolutionary computation - GECCO \textquotesingle14_. doi:[10.1145/2576768.2598264](https://doi.org/10.1145/2576768.2598264)
-
-### F
-- **Falcons**: de Vasconcelos Segundo EH, Mariani VC, dos Santos Coelho L (2019). “Design of heat exchangers using Falcon Optimization Algorithm.” _Applied Thermal Engineering_, *156*, 119-144. doi:[10.1016/j.applthermaleng.2019.04.038](https://doi.org/10.1016/j.applthermaleng.2019.04.038)
-- **Farmland Fertility**: Shayanfar H, Gharehchopogh FS (2018). “Farmland fertility: A new metaheuristic algorithm for solving continuous optimization problems.” _Applied Soft Computing_, *71*, 728-746. doi:[10.1016/j.asoc.2018.07.033](https://doi.org/10.1016/j.asoc.2018.07.033)
-- **FBI**: Chou J, Nguyen N (2020). “FBI inspired meta-optimization.” _Applied Soft Computing_, *93*, 106339. ISSN 1568-4946, doi:[10.1016/j.asoc.2020.106339](https://doi.org/10.1016/j.asoc.2020.106339)
-- **FIFA World Cup**: Razmjooy N, Khalilpour M, Ramezani M (2016). “A New Meta-Heuristic Optimization Algorithm Inspired by FIFA World Cup Competitions: Theory and Its Application in PID Designing for AVR System.” _Journal of Control, Automation and Electrical Systems_, *27*(4), 419-440. doi:[10.1007/s40313-016-0242-6](https://doi.org/10.1007/s40313-016-0242-6)
-- **Fireflies**: Yang X (2009). “Firefly Algorithms for Multimodal Optimization.” In _Stochastic Algorithms: Foundations and Applications_, 169-178. Springer Berlin Heidelberg. doi:[10.1007/978-3-642-04944-6_14](https://doi.org/10.1007/978-3-642-04944-6_14)
-- **Fireworks**: Tan Y, Zhu Y (2010). “Fireworks Algorithm for Optimization.” In _Lecture Notes in Computer Science_, 355-364. Springer Berlin Heidelberg. doi:[10.1007/978-3-642-13495-1_44](https://doi.org/10.1007/978-3-642-13495-1_44)
-- **Fish: Catfish**: Chuang L, Tsai S, Yang C (2011). “Improved binary particle swarm optimization using catfish effect for feature selection.” _Expert Systems with Applications_, *38*(10), 12699-12707. doi:[10.1016/j.eswa.2011.04.057](https://doi.org/10.1016/j.eswa.2011.04.057)
-- **Fish: Cuttlefish**: Eesa A, Abdulazeez A, Orman Z (2013). “Cuttlefish Algorithm - A Novel Bio-Inspired Optimization Algorithm.” _International Journal of Scientific and Engineering Research_, *4*(9), 1978-1986.
-- **Fish: Fish Schools**: Filho CJAB, de Lima Neto FB, Lins AJCC, Nascimento AIS, Lima MP (2008). “A novel search algorithm based on fish school behavior.” In _2008 IEEE International Conference on Systems, Man and Cybernetics_. doi:[10.1109/icsmc.2008.4811695](https://doi.org/10.1109/icsmc.2008.4811695)
-- **Fish: Fish Swarms**: Li X, Qian J (2003). “Studies on Artificial Fish Swarm Optimization Algorithm Based on Decomposition and Coordination Techniques.” _J Circuits Systems_, *1*, 1-6.
-- **Fish: Mouth Brooding**: Jahani E, Chizari M (2018). “Tackling global optimization problems with a novel algorithm — Mouth Brooding Fish algorithm.” _Applied Soft Computing_, *62*, 987-1002. doi:[10.1016/j.asoc.2017.09.035](https://doi.org/10.1016/j.asoc.2017.09.035)
-- **Flower Pollination**: Yang X (2012). “Flower Pollination Algorithm for Global Optimization.” In _Unconventional Computation and Natural Computation_, 240-249. Springer Berlin Heidelberg. doi:[10.1007/978-3-642-32894-7_27](https://doi.org/10.1007/978-3-642-32894-7_27)
-- **Forests: Forest Regeneration**: Moez H, Kaveh A, Taghizadieh N (2016). “Natural Forest Regeneration Algorithm: A New Meta-Heuristic.” _Iranian Journal of Science and Technology, Transactions of Civil Engineering_, *40*(4), 311-326. doi:[10.1007/s40996-016-0042-z](https://doi.org/10.1007/s40996-016-0042-z)
-- **Forests: Tree Survival**: Ghaemi M, Feizi-Derakhshi M (2014). “Forest Optimization Algorithm.” _Expert Systems with Applications_, *41*(15), 6676-6687. doi:[10.1016/j.eswa.2014.05.009](https://doi.org/10.1016/j.eswa.2014.05.009)
-- **Fox: Red Fox**: Połap D, Woźniak M (2021). “Red fox optimization algorithm.” _Expert Systems with Applications_, *166*, 114107. doi:[10.1016/j.eswa.2020.114107](https://doi.org/10.1016/j.eswa.2020.114107)
-- **Fractals**: Salimi H (2015). “Stochastic Fractal Search: A powerful metaheuristic algorithm.” _Knowledge-Based Systems_, *75*, 1-18. doi:[10.1016/j.knosys.2014.07.025](https://doi.org/10.1016/j.knosys.2014.07.025)
-- **Frogs: Japanese Tree Frogs**: Hernández H, Blum C (2012). “Distributed graph coloring: an approach based on the calling behavior of Japanese tree frogs.” _Swarm Intelligence_, *6*(2), 117-150. doi:[10.1007/s11721-012-0067-2](https://doi.org/10.1007/s11721-012-0067-2)
-- **Frogs: Leaping**: Eusuff MM, Lansey KE (2003). “Optimization of Water Distribution Network Design Using the Shuffled Frog Leaping Algorithm.” _Journal of Water Resources Planning and Management_, *129*(3), 210-225. doi:10.1061/(asce)0733-9496(2003)129:3(210)
-- **Fruit Fly**: Pan W (2012). “A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example.” _Knowledge-Based Systems_, *26*, 69-74. doi:[10.1016/j.knosys.2011.07.001](https://doi.org/10.1016/j.knosys.2011.07.001)
-
-### G
-- **Galaxies**: Hosseini HS (2011). “Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation.” _International Journal of Computational Science and Engineering_, *6*(1/2), 132. doi:[10.1504/ijcse.2011.041221](https://doi.org/10.1504/ijcse.2011.041221)
-- **Galaxies: Motion**: Muthiah-Nakarajan V, Noel MM (2016). “Galactic Swarm Optimization: A new global optimization metaheuristic inspired by galactic motion.” _Applied Soft Computing_, *38*, 771-787. doi:[10.1016/j.asoc.2015.10.034](https://doi.org/10.1016/j.asoc.2015.10.034)
-- **Gas Molecules: Brownian Motion**: Abdechiri M, Meybodi MR, Bahrami H (2013). “Gases Brownian Motion Optimization: an Algorithm for Optimization (GBMO).” _Applied Soft Computing_, *13*(5), 2932-2946. doi:[10.1016/j.asoc.2012.03.068](https://doi.org/10.1016/j.asoc.2012.03.068)
-- **Gas Molecules: Kinetic Energy**: Moein S, Logeswaran R (2014). “KGMO: A swarm optimization algorithm based on the kinetic energy of gas molecules.” _Information Sciences_, *275*, 127-144. doi:[10.1016/j.ins.2014.02.026](https://doi.org/10.1016/j.ins.2014.02.026)
-- **Gene Expression**: Ferreira C (2002). “Gene Expression Programming in Problem Solving.” In _Soft Computing and Industry_, 635-653. Springer London. doi:[10.1007/978-1-4471-0123-9_54](https://doi.org/10.1007/978-1-4471-0123-9_54)
-- **General Relativity**: Beiranvand H, Rokrok E (2015). “General Relativity Search Algorithm: A Global Optimization Approach.” _International Journal of Computational Intelligence and Applications_, *14*(03), 1550017. doi:[10.1142/s1469026815500170](https://doi.org/10.1142/s1469026815500170)
-- **Genes**: Holland J (1975). _Adaptation in Natural and Artificial Systems, An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence_. MIT Press.
-- **Genetic Folding**: Mezher M, Abbod M (2011). “Genetic Folding: A New Class of Evolutionary Algorithms.” In Bramer M, Petridis M, Hopgood A (eds.), _Research and Development in Intelligent Systems XXVII_, 279-284. ISBN 978-0-85729-130-1.
-- **Glow Worms**: Krishnanand KN, Ghose D (2008). “Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions.” _Swarm Intelligence_, *3*(2), 87-124. doi:[10.1007/s11721-008-0021-5](https://doi.org/10.1007/s11721-008-0021-5)
-- **Goats: Wild Goats Climbing**: Shefaei A, Mohammadi-Ivatloo B (2018). “Wild Goats Algorithm: An Evolutionary Algorithm to Solve the Real-World Optimization Problems.” _IEEE Transactions on Industrial Informatics_, *14*(7), 2951–2961. ISSN 1941-0050, doi:[10.1109/tii.2017.2779239](https://doi.org/10.1109/tii.2017.2779239)
-- **Golden Jackals**: Chopra N, Mohsin Ansari M (2022). “Golden jackal optimization: A novel nature-inspired optimizer for engineering applications.” _Expert Systems with Applications_, *198*, 116924. ISSN 0957-4174, doi:[10.1016/j.eswa.2022.116924](https://doi.org/10.1016/j.eswa.2022.116924)
-- **Golf**: Montazeri Z, Niknam T, Aghaei J, Malik OP, Dehghani M, Dhiman G (2023). “Golf Optimization Algorithm: A New Game-Based Metaheuristic Algorithm and Its Application to Energy Commitment Problem Considering Resilience.” _Biomimetics_, *8*(5), 386. ISSN 2313-7673, doi:[10.3390/biomimetics8050386](https://doi.org/10.3390/biomimetics8050386)
-- **Gorilla Troops**: Abdollahzadeh B, Soleimanian Gharehchopogh F, Mirjalili S (2021). “Artificial gorilla troops optimizer: A new nature‐inspired metaheuristic algorithm for global optimization problems.” _International Journal of Intelligent Systems_, *36*(10), 5887–5958. ISSN 1098-111X, doi:[10.1002/int.22535](https://doi.org/10.1002/int.22535)
-- **Grasshoppers**: Saremi S, Mirjalili S, Lewis A (2017). “Grasshopper Optimisation Algorithm: Theory and application.” _Advances in Engineering Software_, *105*, 30-47. doi:[10.1016/j.advengsoft.2017.01.004](https://doi.org/10.1016/j.advengsoft.2017.01.004)
-- **Gravitation: Gravitational Laws**: Rashedi E, Nezamabadi-pour H, Saryazdi S (2009). “GSA: A Gravitational Search Algorithm.” _Information Sciences_, *179*(13), 2232-2248. doi:[10.1016/j.ins.2009.03.004](https://doi.org/10.1016/j.ins.2009.03.004)
-- **Gravitation: Interactions**: Flores JJ, López R, Barrera J (2011). “Gravitational Interactions Optimization.” In _Lecture Notes in Computer Science_, 226-237. Springer Berlin Heidelberg. doi:[10.1007/978-3-642-25566-3_17](https://doi.org/10.1007/978-3-642-25566-3_17)
-- **Gravitation:Gravitational Radiation**: Chuang C, Jiang J (2007). “Integrated radiation optimization: inspired by the gravitational radiation in the curvature of space-time.” In _2007 IEEE Congress on Evolutionary Computation_. doi:[10.1109/cec.2007.4424875](https://doi.org/10.1109/cec.2007.4424875)
-- **Great Deluge**: Dueck G (1993). “New Optimization Heuristics: The Great Deluge and Record to Record Travel.” _Journal of Computational Physics_, *104*(1), 86-92. doi:[10.1006/jcph.1993.1010](https://doi.org/10.1006/jcph.1993.1010)
-- **Grenades**: Ahrari A, Atai AA (2010). “Grenade Explosion Method—A novel tool for optimization of multimodal functions.” _Applied Soft Computing_, *10*(4), 1132-1140. doi:[10.1016/j.asoc.2009.11.032](https://doi.org/10.1016/j.asoc.2009.11.032)
-- **Group Counselling**: Eita MA, Fahmy MM (2009). “Group Counseling Optimization: A Novel Approach.” In _Research and Development in Intelligent Systems XXVI_, 195-208. Springer London. doi:[10.1007/978-1-84882-983-1_14](https://doi.org/10.1007/978-1-84882-983-1_14)
-- **Group Decision-Making**: Zhang Q, Wang R, Yang J, Ding K, Li Y, Hu J (2017). “Collective decision optimization algorithm: A new heuristic optimization method.” _Neurocomputing_, *221*, 123-137. doi:[10.1016/j.neucom.2016.09.068](https://doi.org/10.1016/j.neucom.2016.09.068)
-
-### H
-- **Hawks: Harris's Hawk**: DeBruyne AS, Kaur BD (2016). “Harris's Hawk Multi-Objective Optimizer for Reference Point Problems.” In _Proceedings on the International Conference on Artificial Intelligence (ICAI)_, 287-292.
-- **Heart**: Hatamlou A (2014). “Heart: a novel optimization algorithm for cluster analysis.” _Progress in Artificial Intelligence_, *2*(2-3), 167-173. doi:[10.1007/s13748-014-0046-5](https://doi.org/10.1007/s13748-014-0046-5)
-- **Heat Transfers**: Patel VK, Savsani VJ (2015). “Heat transfer search (HTS): a novel optimization algorithm.” _Information Sciences_, *324*, 217–246. ISSN 0020-0255, doi:[10.1016/j.ins.2015.06.044](https://doi.org/10.1016/j.ins.2015.06.044)
-- **Henry’s Law**: Hashim FA, Houssein EH, Mabrouk MS, Al-Atabany W, Mirjalili S (2019). “Henry gas solubility optimization: A novel physics-based algorithm.” _Future Generation Computer Systems_, *101*, 646–667. ISSN 0167-739X, doi:[10.1016/j.future.2019.07.015](https://doi.org/10.1016/j.future.2019.07.015)
-- **Herds: Selfish**: Fausto F, Cuevas E, Valdivia A, González A (2017). “A global optimization algorithm inspired in the behavior of selfish herds.” _Biosystems_, *160*, 39-55. doi:[10.1016/j.biosystems.2017.07.010](https://doi.org/10.1016/j.biosystems.2017.07.010)
-- **Hippopotamus**: Amiri MH, Mehrabi Hashjin N, Montazeri M, Mirjalili S, Khodadadi N (2024). “Hippopotamus optimization algorithm: a novel nature-inspired optimization algorithm.” _Scientific Reports_, *14*(1). ISSN 2045-2322, doi:[10.1038/s41598-024-54910-3](https://doi.org/10.1038/s41598-024-54910-3)
-- **Honey Badgers**: Hashim FA, Houssein EH, Hussain K, Mabrouk MS, Al-Atabany W (2022). “Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems.” _Mathematics and Computers in Simulation_, *192*, 84–110. ISSN 0378-4754, doi:[10.1016/j.matcom.2021.08.013](https://doi.org/10.1016/j.matcom.2021.08.013)
-- **Hoopoe**: El-Dosuky M, El-Bassiouny A, Hamza T, Rashad M (2012). “New Hoopoe Heuristic Optimization.” _International Journal of Science and Advanced Technology_, *2*(9), 85-90.
-- **Hormones**: Zheng K, Tang D, Giret A, Salido MA, Sang Z (2016). “A hormone regulation—based approach for distributed and on-line scheduling of machines and automated guided vehicles.” _Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture_, *232*(1), 99-113. doi:[10.1177/0954405416662078](https://doi.org/10.1177/0954405416662078)
-- **Horses: Hierarchical Organization**: Moldovan D (2020). “Horse Optimization Algorithm: A Novel Bio-Inspired Algorithm for Solving Global Optimization Problems.” In _Advances in Intelligent Systems and Computing_, 195-209. Springer International Publishing. doi:[10.1007/978-3-030-51971-1_16](https://doi.org/10.1007/978-3-030-51971-1_16)
-- **Horses: Wild**: Naruei I, Keynia F (2021). “Wild horse optimizer: a new meta-heuristic algorithm for solving engineering optimization problems.” _Engineering with Computers_, *38*(S4), 3025–3056. ISSN 1435-5663, doi:[10.1007/s00366-021-01438-z](https://doi.org/10.1007/s00366-021-01438-z)
-- **Humans: Cooking**: Trojovská E, Dehghani M (2022). “A new human-based metahurestic optimization method based on mimicking cooking training.” _Scientific Reports_, *12*(1). ISSN 2045-2322, doi:[10.1038/s41598-022-19313-2](https://doi.org/10.1038/s41598-022-19313-2)
-- **Humans: Hunting**: Brammya G, Praveena S, Preetha NSN, Ramya R, Rajakumar BR, Binu D (2019). “Deer Hunting Optimization Algorithm: A New Nature-Inspired Meta-heuristic Paradigm.” _The Computer Journal_. doi:[10.1093/comjnl/bxy133](https://doi.org/10.1093/comjnl/bxy133)
-- **Humans: Life Choices**: Khatri A, Gaba A, Rana KPS, Kumar V (2019). “A novel life choice-based optimizer.” _Soft Computing_, *24*(12), 9121-9141. doi:[10.1007/s00500-019-04443-z](https://doi.org/10.1007/s00500-019-04443-z)
-- **Humans: Reproduction**: Acharya D, Das DK (2022). “A novel Human Conception Optimizer for solving optimization problems.” _Scientific Reports_, *12*(1). ISSN 2045-2322, doi:[10.1038/s41598-022-25031-6](https://doi.org/10.1038/s41598-022-25031-6)
-- **Humans: Search and Rescue**: Shabani A, Asgarian B, Gharebaghi SA, Salido MA, Giret A (2019). “A New Optimization Algorithm Based on Search and Rescue Operations.” _Mathematical Problems in Engineering_, *2019*, 1-23. doi:[10.1155/2019/2482543](https://doi.org/10.1155/2019/2482543)
-- **Humans: Students**: Das B, Mukherjee V, Das D (2020). “Student psychology based optimization algorithm: A new population based optimization algorithm for solving optimization problems.” _Advances in Engineering Software_, *146*, 102804. doi:[10.1016/j.advengsoft.2020.102804](https://doi.org/10.1016/j.advengsoft.2020.102804)
-- **Hummingbirds**: Zhao W, Wang L, Mirjalili S (2022). “Artificial hummingbird algorithm: A new bio-inspired optimizer with its engineering applications.” _Computer Methods in Applied Mechanics and Engineering_, *388*, 114194. ISSN 0045-7825, doi:[10.1016/j.cma.2021.114194](https://doi.org/10.1016/j.cma.2021.114194)
-- **Hyenas**: Dhiman G, Kumar V (2017). “Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications.” _Advances in Engineering Software_, *114*, 48-70. doi:[10.1016/j.advengsoft.2017.05.014](https://doi.org/10.1016/j.advengsoft.2017.05.014)
-
-### I
-- **Interior Design**: Gandomi AH (2014). “Interior search algorithm (ISA): A novel approach for global optimization.” _ISA Transactions_, *53*(4), 1168-1183. doi:[10.1016/j.isatra.2014.03.018](https://doi.org/10.1016/j.isatra.2014.03.018)
-- **Invasive Weeds**: Mehrabian A, Lucas C (2006). “A novel numerical optimization algorithm inspired from weed colonization.” _Ecological Informatics_, *1*(4), 355-366. doi:[10.1016/j.ecoinf.2006.07.003](https://doi.org/10.1016/j.ecoinf.2006.07.003)
-- **Ions**: Javidy B, Hatamlou A, Mirjalili S (2015). “Ions motion algorithm for solving optimization problems.” _Applied Soft Computing_, *32*, 72-79. doi:[10.1016/j.asoc.2015.03.035](https://doi.org/10.1016/j.asoc.2015.03.035)
-
-### J
-- **Jaguars**: Chen C, Tsai Y, Liu I, Lai C, Yeh Y, Kuo S, Chou Y (2015). “A Novel Metaheuristic: Jaguar Algorithm with Learning Behavior.” In _2015 IEEE International Conference on Systems, Man, and Cybernetics_. doi:[10.1109/smc.2015.282](https://doi.org/10.1109/smc.2015.282)
-- **Jellyfish**: Chou J, Truong D (2021). “A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean.” _Applied Mathematics and Computation_, *389*, 125535. ISSN 0096-3003, doi:[10.1016/j.amc.2020.125535](https://doi.org/10.1016/j.amc.2020.125535)
-
-### K
-- **Kepler’s Laws**: Abdel-Basset M, Mohamed R, Azeem SAA, Jameel M, Abouhawwash M (2023). “Kepler optimization algorithm: A new metaheuristic algorithm inspired by Kepler’s laws of planetary motion.” _Knowledge-Based Systems_, *268*, 110454. ISSN 0950-7051, doi:[10.1016/j.knosys.2023.110454](https://doi.org/10.1016/j.knosys.2023.110454)
-- **Keshtel Duck**: Hajiaghaei-Keshteli M, Aminnayeri M (2014). “Solving the integrated scheduling of production and rail transportation problem by Keshtel algorithm.” _Applied Soft Computing_, *25*, 184-203. doi:[10.1016/j.asoc.2014.09.034](https://doi.org/10.1016/j.asoc.2014.09.034)
-- **Kestrels**: Agbehadji IE, Millham R, Fong S (2016). “Kestrel-Based Search Algorithm for Association Rule Mining and Classification of Frequently Changed Items.” In _2016 8th International Conference on Computational Intelligence and Communication Networks (CICN)_. doi:[10.1109/cicn.2016.76](https://doi.org/10.1109/cicn.2016.76)
-- **Kidneys**: Jaddi NS, Alvankarian J, Abdullah S (2017). “Kidney-inspired algorithm for optimization problems.” _Communications in Nonlinear Science and Numerical Simulation_, *42*, 358-369. doi:[10.1016/j.cnsns.2016.06.006](https://doi.org/10.1016/j.cnsns.2016.06.006)
-- **Komodos**: Jati GK, Kuwanto G, Hashmi T, Widjaja H (2023). “Discrete komodo algorithm for traveling salesman problem.” _Applied Soft Computing_, *139*, 110219. ISSN 1568-4946, doi:[10.1016/j.asoc.2023.110219](https://doi.org/10.1016/j.asoc.2023.110219)
-- **Kookaburras**: Dehghani M, Montazeri Z, Bektemyssova G, Malik OP, Dhiman G, Ahmed AEM (2023). “Kookaburra Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems.” _Biomimetics_, *8*(6), 470. ISSN 2313-7673, doi:[10.3390/biomimetics8060470](https://doi.org/10.3390/biomimetics8060470)
-- **Krill**: Gandomi AH, Alavi AH (2012). “Krill herd: A new bio-inspired optimization algorithm.” _Communications in Nonlinear Science and Numerical Simulation_, *17*(12), 4831-4845. doi:[10.1016/j.cnsns.2012.05.010](https://doi.org/10.1016/j.cnsns.2012.05.010)
-
-### L
-- **Ladybirds**: Wang P, Zhu Z, Huang S (2013). “Seven-Spot Ladybird Optimization: A Novel and Efficient Metaheuristic Algorithm for Numerical Optimization.” _The Scientific World Journal_, *2013*, 1-11. doi:[10.1155/2013/378515](https://doi.org/10.1155/2013/378515)
-- **Lemurs**: Abasi AK, Makhadmeh SN, Al-Betar MA, Alomari OA, Awadallah MA, Alyasseri ZAA, Doush IA, Elnagar A, Alkhammash EH, Hadjouni M (2022). “Lemurs Optimizer: A New Metaheuristic Algorithm for Global Optimization.” _Applied Sciences_, *12*(19), 10057. ISSN 2076-3417, doi:[10.3390/app121910057](https://doi.org/10.3390/app121910057)
-- **Lightning**: Shareef H, Ibrahim AA, Mutlag AH (2015). “Lightning search algorithm.” _Applied Soft Computing_, *36*, 315-333. doi:[10.1016/j.asoc.2015.07.028](https://doi.org/10.1016/j.asoc.2015.07.028)
-- **Lions: Pride**: Wang B, Jin X, Cheng B (2012). “Lion pride optimizer: An optimization algorithm inspired by lion pride behavior.” _Science China Information Sciences_, *55*(10), 2369-2389. doi:[10.1007/s11432-012-4548-0](https://doi.org/10.1007/s11432-012-4548-0)
-- **Lions: Swarm**: Liu J, Li D, Wu Y, Liu D (2020). “Lion swarm optimization algorithm for comparative study with application to optimal dispatch of cascade hydropower stations.” _Applied Soft Computing_, *87*, 105974. ISSN 1568-4946, doi:[10.1016/j.asoc.2019.105974](https://doi.org/10.1016/j.asoc.2019.105974)
-- **Lizards**: Kumar N, Singh N, Vidyarthi DP (2021). “Artificial lizard search optimization (ALSO): a novel nature-inspired meta-heuristic algorithm.” _Soft Computing_, *25*(8), 6179–6201. ISSN 1433-7479, doi:[10.1007/s00500-021-05606-7](https://doi.org/10.1007/s00500-021-05606-7)
-- **Locusts**: Chen S (2009). “An Analysis of Locust Swarms on Large Scale Global Optimization Problems.” In _Artificial Life: Borrowing from Biology_, 211-220. Springer Berlin Heidelberg. doi:[10.1007/978-3-642-10427-5_21](https://doi.org/10.1007/978-3-642-10427-5_21)
-- **Lyrebirds**: Dehghani M, Bektemyssova G, Montazeri Z, Shaikemelev G, Malik OP, Dhiman G (2023). “Lyrebird Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems.” _Biomimetics_, *8*(6), 507. ISSN 2313-7673, doi:[10.3390/biomimetics8060507](https://doi.org/10.3390/biomimetics8060507)
-
-### M
-- **Manta Rays**: Zhao W, Zhang Z, Wang L (2020). “Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications.” _Engineering Applications of Artificial Intelligence_, *87*, 103300. ISSN 0952-1976, doi:[10.1016/j.engappai.2019.103300](https://doi.org/10.1016/j.engappai.2019.103300)
-- **Marine Predators**: Faramarzi A, Heidarinejad M, Mirjalili S, Gandomi AH (2020). “Marine Predators Algorithm: A nature-inspired metaheuristic.” _Expert Systems with Applications_, *152*, 113377. doi:[10.1016/j.eswa.2020.113377](https://doi.org/10.1016/j.eswa.2020.113377)
-- **Markets**: Ghorbani N, Babaei E (2014). “Exchange market algorithm.” _Applied Soft Computing_, *19*, 177-187. doi:[10.1016/j.asoc.2014.02.006](https://doi.org/10.1016/j.asoc.2014.02.006)
-- **Mayflies**: Zervoudakis K, Tsafarakis S (2020). “A mayfly optimization algorithm.” _Computers & Industrial Engineering_, *145*, 106559. doi:[10.1016/j.cie.2020.106559](https://doi.org/10.1016/j.cie.2020.106559)
-- **Meerkats**: Klein CE, dos Santos Coelho L (2018). “Meerkats-inspired Algorithm for Global Optimization Problems.” In _Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning_.
-- **Mice: Wild Mice**: Nejatian S, Omidvar R, Parvin H, Rezaei V, Yasrebi M (2019). “A New Algorithm: Wild Mice Colony Algorithm (WMC).” _TABRIZ JOURNAL OF ELECTRICAL ENGINEERING_.
-- **Mine Explosions**: Sadollah A, Bahreininejad A, Eskandar H, Hamdi M (2012). “Mine blast algorithm for optimization of truss structures with discrete variables.” _Computers & Structures_, *102-103*, 49-63. doi:[10.1016/j.compstruc.2012.03.013](https://doi.org/10.1016/j.compstruc.2012.03.013)
-- **Molecular Dynamics**: Zhao W, Wang L, Zhang Z (2019). “Atom search optimization and its application to solve a hydrogeologic parameter estimation problem.” _Knowledge-Based Systems_, *163*, 283-304. doi:[10.1016/j.knosys.2018.08.030](https://doi.org/10.1016/j.knosys.2018.08.030)
-- **Monkeys: Monkey Foraging**: Mucherino A, Seref O, Seref O, Kundakcioglu OE, Pardalos P (2007). “Monkey search: a novel metaheuristic search for global optimization.” In _AIP Conference Proceedings_. doi:[10.1063/1.2817338](https://doi.org/10.1063/1.2817338)
-- **Monkeys: Red Colobuses Monkeys**: AL-kubaisy WJ, Yousif M, Al-Khateeb B, Mahmood M, Le D (2021). “The Red Colobuses Monkey: A New Nature–Inspired Metaheuristic Optimization Algorithm.” _International Journal of Computational Intelligence Systems_, *14*(1), 1108. ISSN 1875-6883, doi:[10.2991/ijcis.d.210301.004](https://doi.org/10.2991/ijcis.d.210301.004)
-- **Monkeys: Spider Monkeys**: Bansal JC, Sharma H, Jadon SS, Clerc M (2014). “Spider Monkey Optimization algorithm for numerical optimization.” _Memetic Computing_, *6*(1), 31-47. doi:[10.1007/s12293-013-0128-0](https://doi.org/10.1007/s12293-013-0128-0)
-- **Mosquitos: Egg-laying Behavior**: ul Amir Afsar Minhas F, Arif M (2011). “MOX: A novel global optimization algorithm inspired from Oviposition site selection and egg hatching inhibition in mosquitoes.” _Applied Soft Computing_, *11*(8), 4614-4625. doi:[10.1016/j.asoc.2011.07.020](https://doi.org/10.1016/j.asoc.2011.07.020)
-- **Mosquitos: Flying Behavior**: Alauddin M (2016). “Mosquito flying optimization (MFO).” In _2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)_. doi:[10.1109/iceeot.2016.7754783](https://doi.org/10.1109/iceeot.2016.7754783)
-- **Moths**: Mirjalili S (2015). “Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm.” _Knowledge-Based Systems_, *89*, 228-249. doi:[10.1016/j.knosys.2015.07.006](https://doi.org/10.1016/j.knosys.2015.07.006)
-- **Mountain Climbers**: Zhang LM, Dahlmann C, Zhang Y (2009). “Human-Inspired Algorithms for continuous function optimization.” In _2009 IEEE International Conference on Intelligent Computing and Intelligent Systems_. doi:[10.1109/icicisys.2009.5357838](https://doi.org/10.1109/icicisys.2009.5357838)
-- **Mountain Gazelles**: Abdollahzadeh B, Gharehchopogh FS, Khodadadi N, Mirjalili S (2022). “Mountain Gazelle Optimizer: A new Nature-inspired Metaheuristic Algorithm for Global Optimization Problems.” _Advances in Engineering Software_, *174*, 103282. ISSN 0965-9978, doi:[10.1016/j.advengsoft.2022.103282](https://doi.org/10.1016/j.advengsoft.2022.103282)
-- **Multiverse**: Mirjalili S, Mirjalili SM, Hatamlou A (2015). “Multi-Verse Optimizer: a nature-inspired algorithm for global optimization.” _Neural Computing and Applications_, *27*(2), 495-513. doi:[10.1007/s00521-015-1870-7](https://doi.org/10.1007/s00521-015-1870-7)
-- **Mushroom Reproduction**: Bidar M, Kanan HR, Mouhoub M, Sadaoui S (2018). “Mushroom Reproduction Optimization (MRO): A Novel Nature-Inspired Evolutionary Algorithm.” In _2018 IEEE Congress on Evolutionary Computation_.
-- **Musicians**: Geem ZW, Kim JH, Loganathan G (2001). “A New Heuristic Optimization Algorithm: Harmony Search.” _SIMULATION_, *76*(2), 60-68. doi:[10.1177/003754970107600201](https://doi.org/10.1177/003754970107600201)
-
-### N
-- **Naked Mole Rats: Mating**: Salgotra R, Singh U (2019). “The naked mole-rat algorithm.” _Neural Computing and Applications_, *31*(12), 8837–8857. ISSN 1433-3058, doi:[10.1007/s00521-019-04464-7](https://doi.org/10.1007/s00521-019-04464-7)
-- **Naked Mole Rats: Social Behaviors**: Taherdangkoo M, Shirzadi MH, Yazdi M, Bagheri MH (2013). “A robust clustering method based on blind, naked mole-rats (BNMR) algorithm.” _Swarm and Evolutionary Computation_, *10*, 1-11. doi:[10.1016/j.swevo.2013.01.001](https://doi.org/10.1016/j.swevo.2013.01.001)
-- **Neurons**: Asil Gharebaghi S, Ardalan Asl M (2017). “NEW META-HEURISTIC OPTIMIZATION ALGORITHM USING NEURONAL COMMUNICATION.” _International Journal of Optimization in Civil Engineering_, *7*(3). http://ijoce.iust.ac.ir/article-1-306-en.pdf, .
-- **Newton's Cooling Law**: Kaveh A, Dadras A (2017). “A novel meta-heuristic optimization algorithm: Thermal exchange optimization.” _Advances in Engineering Software_, *110*, 69-84. doi:[10.1016/j.advengsoft.2017.03.014](https://doi.org/10.1016/j.advengsoft.2017.03.014)
-- **Nuclear Collision**: Sacco WF, Oliveira C (2005). “A new stochastic optimization algorithm based on a particle collision metaheuristic.” _Proceedings of 6th WCSMO_.
-
-### O
-- **Optics**: Kashan AH (2015). “A new metaheuristic for optimization: Optics inspired optimization (OIO).” _Computers & Operations Research_, *55*, 99-125. doi:[10.1016/j.cor.2014.10.011](https://doi.org/10.1016/j.cor.2014.10.011)
-- **Owls**: de Vasconcelos Segundo EH, Mariani VC, dos Santos Coelho L (2019). “Metaheuristic inspired on owls behavior applied to heat exchangers design.” _Thermal Science and Engineering Progress_, *14*, 100431. doi:[10.1016/j.tsep.2019.100431](https://doi.org/10.1016/j.tsep.2019.100431)
-
-### P
-- **Paddy Fields**: Premaratne U, Samarabandu J, Sidhu T (2009). “A new biologically inspired optimization algorithm.” In _2009 International Conference on Industrial and Information Systems (ICIIS)_. doi:[10.1109/iciinfs.2009.5429852](https://doi.org/10.1109/iciinfs.2009.5429852)
-- **Pearl Hunting**: Chan CY, Xue F, Ip WH, Cheung CF (2012). “A Hyper-Heuristic Inspired by Pearl Hunting.” In _Lecture Notes in Computer Science_, 349-353. Springer Berlin Heidelberg. doi:[10.1007/978-3-642-34413-8_26](https://doi.org/10.1007/978-3-642-34413-8_26)
-- **Pelicans**: Trojovský P, Dehghani M (2022). “Pelican Optimization Algorithm: A Novel Nature-Inspired Algorithm for Engineering Applications.” _Sensors_, *22*(3), 855. ISSN 1424-8220, doi:[10.3390/s22030855](https://doi.org/10.3390/s22030855)
-- **Penguins**: Gheraibia Y, Moussaoui A (2013). “Penguins Search Optimization Algorithm (PeSOA).” In _Recent Trends in Applied Artificial Intelligence_, 222-231. Springer Berlin Heidelberg. doi:[10.1007/978-3-642-38577-3_23](https://doi.org/10.1007/978-3-642-38577-3_23)
-- **Penguins: Emperor Penguins**: Dhiman G, Kumar V (2018). “Emperor penguin optimizer: A bio-inspired algorithm for engineering problems.” _Knowledge-Based Systems_, *159*, 20-50. doi:[10.1016/j.knosys.2018.06.001](https://doi.org/10.1016/j.knosys.2018.06.001)
-- **Pigeons**: Duan H, Qiao P (2014). “Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning.” _International Journal of Intelligent Computing and Cybernetics_, *7*(1), 24-37.
-- **Pigeons: Feeding**: Lamy J (2018). “Artificial Feeding Birds (AFB): A New Metaheuristic Inspired by the Behavior of Pigeons.” In _Advances in Nature-Inspired Computing and Applications_, 43-60. Springer International Publishing. doi:[10.1007/978-3-319-96451-5_3](https://doi.org/10.1007/978-3-319-96451-5_3)
-- **Pigs: Duroc Pigs**: Czerniak JM, Zarzycki H, Ewald D, Augustyn P (2020). “Application of OFN Numbers in the Artificial Duroc Pigs Optimization (ADPO) Method.” In _Uncertainty and Imprecision in Decision Making and Decision Support: New Challenges, Solutions and Perspectives_, 310–327. ISBN 9783030470241, doi:[10.1007/978-3-030-47024-1_31](https://doi.org/10.1007/978-3-030-47024-1_31)
-- **Plants: Plant Growth**: Li J, Cui Z, Shi Z (2012). “An Improved Artificial Plant Optimization Algorithm for Coverage Problem in WSN.” _Sensor Letters_, *10*(8), 1874-1878. doi:[10.1166/sl.2012.2627](https://doi.org/10.1166/sl.2012.2627)
-- **Plants: Plant Intelligence**: Akyol S, Alatas B (2016). “Plant intelligence based metaheuristic optimization algorithms.” _Artificial Intelligence Review_, *47*(4), 417-462. doi:[10.1007/s10462-016-9486-6](https://doi.org/10.1007/s10462-016-9486-6)
-- **Plants: Plant Propagation**: Sulaiman M, Salhi A, Selamoglu BI, Kirikchi OB (2014). “A Plant Propagation Algorithm for Constrained Engineering Optimisation Problems.” _Mathematical Problems in Engineering_, *2014*, 1-10. doi:[10.1155/2014/627416](https://doi.org/10.1155/2014/627416)
-- **Plants: Sapling Growth**: Karci A, Alatas B (2006). “Thinking capability of saplings growing up algorithm.” In _International Conference on Intelligent Data Engineering and Automated Learning_, 386-393. Springer.
-- **Polar Bears**: Połap D, Woz\textasciiacuteniak M (2017). “Polar Bear Optimization Algorithm: Meta-Heuristic with Fast Population Movement and Dynamic Birth and Death Mechanism.” _Symmetry_, *9*(10), 203. doi:[10.3390/sym9100203](https://doi.org/10.3390/sym9100203)
-- **Politics: Imperialism**: Atashpaz-Gargari E, Lucas C (2007). “Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition.” In _2007 IEEE Congress on Evolutionary Computation_. doi:[10.1109/cec.2007.4425083](https://doi.org/10.1109/cec.2007.4425083)
-- **Politics: Parliamentarist Elections**: Borji A (2007). “A New Global Optimization Algorithm Inspired by Parliamentary Political Competitions.” In _MICAI 2007: Advances in Artificial Intelligence_, 61-71. Springer Berlin Heidelberg. doi:[10.1007/978-3-540-76631-5_7](https://doi.org/10.1007/978-3-540-76631-5_7)
-- **Politics: Presidential Elections**: Emami H, Derakhshan F (2015). “Election algorithm: A new socio-politically inspired strategy.” _AI Communications_, *28*(3), 591–603. ISSN 18758452, 09217126, doi:[10.3233/AIC-140652](https://doi.org/10.3233/AIC-140652)
-- **Politics: Strategies**: Melvix JL (2014). “Greedy Politics Optimization: Metaheuristic inspired by political strategies adopted during state assembly elections.” In _2014 IEEE International Advance Computing Conference (IACC)_. doi:[10.1109/iadcc.2014.6779490](https://doi.org/10.1109/iadcc.2014.6779490)
-- **Pufferfish**: Al-Baik O, Alomari S, Alssayed O, Gochhait S, Leonova I, Dutta U, Malik OP, Montazeri Z, Dehghani M (2024). “Pufferfish Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems.” _Biomimetics_, *9*(2), 65. ISSN 2313-7673, doi:[10.3390/biomimetics9020065](https://doi.org/10.3390/biomimetics9020065)
-- **Pumas**: Abdollahzadeh B, Khodadadi N, Barshandeh S, Trojovský P, Gharehchopogh FS, El-kenawy EM, Abualigah L, Mirjalili S (2024). “Puma optimizer (PO): a novel metaheuristic optimization algorithm and its application in machine learning.” _Cluster Computing_. ISSN 1573-7543, doi:[10.1007/s10586-023-04221-5](https://doi.org/10.1007/s10586-023-04221-5)
-
-### Q
-- **Quantum Superposition**: Saire JEC, Tupac VYJ (2015). “An approach to real-coded quantum inspired evolutionary algorithm using particles filter.” In _2015 Latin America Congress on Computational Intelligence (LA-CCI)_. doi:[10.1109/la-cci.2015.7435984](https://doi.org/10.1109/la-cci.2015.7435984)
-
-### R
-- **Rabbits**: Wang L, Cao Q, Zhang Z, Mirjalili S, Zhao W (2022). “Artificial rabbits optimization: A new bio-inspired meta-heuristic algorithm for solving engineering optimization problems.” _Engineering Applications of Artificial Intelligence_, *114*, 105082. ISSN 0952-1976, doi:[10.1016/j.engappai.2022.105082](https://doi.org/10.1016/j.engappai.2022.105082)
-- **Raccoons**: Zangbari Koohi S, Abdul Hamid NAW, Othman M, Ibragimov G (2019). “Raccoon Optimization Algorithm.” _IEEE Access_, *7*, 5383–5399. ISSN 2169-3536, doi:[10.1109/access.2018.2882568](https://doi.org/10.1109/access.2018.2882568)
-- **Ravens**: Torabi S, Safi-Esfahani F (2017). “Improved Raven Roosting Optimization algorithm (IRRO).” _Swarm and Evolutionary Computation_. doi:[10.1016/j.swevo.2017.11.006](https://doi.org/10.1016/j.swevo.2017.11.006)
-- **Rays of Light**: Kaveh A, Khayatazad M (2012). “A new meta-heuristic method: Ray Optimization.” _Computers & Structures_, *112-113*, 283-294. doi:[10.1016/j.compstruc.2012.09.003](https://doi.org/10.1016/j.compstruc.2012.09.003)
-- **Reincarnation**: Sharma A (2010). “A new optimizing algorithm using reincarnation concept.” In _2010 11th International Symposium on Computational Intelligence and Informatics (CINTI)_. doi:[10.1109/cinti.2010.5672231](https://doi.org/10.1109/cinti.2010.5672231)
-- **Rhinoceros**: Wang G, Gao X, Zenger K, Coelho LdS (2016). “A novel metaheuristic algorithm inspired by rhino herd behavior.” In _Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016_, number 142, 1026-1033. Linköping University Electronic Press.
-- **Rice: Hybrid**: Ye Z, Ma L, Chen H (2016). “A hybrid rice optimization algorithm.” In _2016 11th International Conference on Computer Science & Education (ICCSE)_. doi:[10.1109/iccse.2016.7581575](https://doi.org/10.1109/iccse.2016.7581575)
-- **River Formation**: Rabanal P, Rodr\'\iguez I, Rubio F (2007). “Using River Formation Dynamics to Design Heuristic Algorithms.” In _Lecture Notes in Computer Science_, 163-177. Springer Berlin Heidelberg. doi:[10.1007/978-3-540-73554-0_16](https://doi.org/10.1007/978-3-540-73554-0_16)
-- **Roach Infestations**: Havens TC, Spain CJ, Salmon NG, Keller JM (2008). “Roach Infestation Optimization.” In _2008 IEEE Swarm Intelligence Symposium_. doi:[10.1109/sis.2008.4668317](https://doi.org/10.1109/sis.2008.4668317)
-- **Roots**: Merrikh-Bayat F (2015). “The runner-root algorithm: A metaheuristic for solving unimodal and multimodal optimization problems inspired by runners and roots of plants in nature.” _Applied Soft Computing_, *33*, 292-303. doi:[10.1016/j.asoc.2015.04.048](https://doi.org/10.1016/j.asoc.2015.04.048)
-
-### S
-- **Sailfish**: Shadravan S, Naji H, Bardsiri V (2019). “The Sailfish Optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems.” _Engineering Applications of Artificial Intelligence_, *80*, 20-34. doi:[10.1016/j.engappai.2019.01.001](https://doi.org/10.1016/j.engappai.2019.01.001)
-- **Salmon Migrations**: Mozaffari A, Fathi A, Behzadipour S (2012). “The great salmon run: a novel bio-inspired algorithm for artificial system design and optimisation.” _International Journal of Bio-Inspired Computation_, *4*(5), 286-301.
-- **Salp Planktons**: Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017). “Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems.” _Advances in Engineering Software_, *114*, 163-191. doi:[10.1016/j.advengsoft.2017.07.002](https://doi.org/10.1016/j.advengsoft.2017.07.002)
-- **Sandpiper**: Kaur A, Jain S, Goel S (2019). “Sandpiper optimization algorithm: a novel approach for solving real-life engineering problems.” _Applied Intelligence_, *50*(2), 582-619. doi:[10.1007/s10489-019-01507-3](https://doi.org/10.1007/s10489-019-01507-3)
-- **Satin Bowerbirds**: Samareh Moosavi SH, Khatibi Bardsiri V (2017). “Satin bowerbird optimizer: A new optimization algorithm to optimize ANFIS for software development effort estimation.” _Engineering Applications of Artificial Intelligence_, *60*, 1–15. ISSN 0952-1976, doi:[10.1016/j.engappai.2017.01.006](https://doi.org/10.1016/j.engappai.2017.01.006)
-- **Scientific Method**: Felipe D, Goldbarg EFG, Goldbarg MC (2014). “Scientific algorithms for the Car Renter Salesman Problem.” In _2014 IEEE Congress on Evolutionary Computation (CEC)_. doi:[10.1109/cec.2014.6900556](https://doi.org/10.1109/cec.2014.6900556)
-- **Sea Lions**: Masadeh R, A. B, Sharieh A (2019). “Sea Lion Optimization Algorithm.” _International Journal of Advanced Computer Science and Applications_, *10*(5). ISSN 2158-107X, doi:[10.14569/ijacsa.2019.0100548](https://doi.org/10.14569/ijacsa.2019.0100548)
-- **Seagulls**: Dhiman G, Kumar V (2019). “Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems.” _Knowledge-Based Systems_, *165*, 169-196. doi:[10.1016/j.knosys.2018.11.024](https://doi.org/10.1016/j.knosys.2018.11.024)
-- **See-See Partridges**: Omidvar R, Parvin H, Rad F (2015). “SSPCO Optimization Algorithm (See-See Partridge Chicks Optimization).” In _2015 Fourteenth Mexican International Conference on Artificial Intelligence (MICAI)_. doi:[10.1109/micai.2015.22](https://doi.org/10.1109/micai.2015.22)
-- **Sharks: Hammerhead**: Ali A, Zafar K, Bakhshi T (2019). “On Nature-Inspired Dynamic Route Planning: Hammerhead Shark Optimization Algorithm.” In _2019 15th International Conference on Emerging Technologies (ICET)_. doi:[10.1109/icet48972.2019.8994757](https://doi.org/10.1109/icet48972.2019.8994757)
-- **Sharks: Smell**: Abedinia O, Amjady N, Ghasemi A (2014). “A new metaheuristic algorithm based on shark smell optimization.” _Complexity_, *21*(5), 97-116. doi:[10.1002/cplx.21634](https://doi.org/10.1002/cplx.21634)
-- **Sharks: White**: Braik M, Hammouri A, Atwan J, Al-Betar MA, Awadallah MA (2022). “White Shark Optimizer: A novel bio-inspired meta-heuristic algorithm for global optimization problems.” _Knowledge-Based Systems_, *243*, 108457. ISSN 0950-7051, doi:[10.1016/j.knosys.2022.108457](https://doi.org/10.1016/j.knosys.2022.108457)
-- **Sheep Flocks**: Kim H, Ahn B (2001). “A new evolutionary algorithm based on sheep flocks heredity model.” In _2001 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (IEEE Cat. No.01CH37233)_. doi:[10.1109/pacrim.2001.953683](https://doi.org/10.1109/pacrim.2001.953683)
-- **Sine Waves**: Tanyildizi E, Demir G (2017). “Golden sine algorithm: a novel math-inspired algorithm.” _Advances in Electrical and Computer Engineering_, *17*(2), 71-79.
-- **Slime Mold**: Monismith DR, Mayfield BE (2008). “Slime Mold as a model for numerical optimization.” In _2008 IEEE Swarm Intelligence Symposium_. doi:[10.1109/sis.2008.4668295](https://doi.org/10.1109/sis.2008.4668295)
-- **Small World**: Du H, Wu X, Zhuang J (2006). “Small-World Optimization Algorithm for Function Optimization.” In _Lecture Notes in Computer Science_, 264-273. Springer Berlin Heidelberg. doi:[10.1007/11881223_33](https://doi.org/10.1007/11881223_33)
-- **Snakes**: Hashim FA, Hussien AG (2022). “Snake Optimizer: A novel meta-heuristic optimization algorithm.” _Knowledge-Based Systems_, *242*, 108320.
-- **Soccer: League**: Moosavian N, Roodsari BK (2014). “Soccer League Competition Algorithm, a New Method for Solving Systems of Nonlinear Equations.” _International Journal of Intelligence Science_, *04*(01), 7-16. doi:[10.4236/ijis.2014.41002](https://doi.org/10.4236/ijis.2014.41002)
-- **Soccer: Modified Soccer Games**: Ahmed ZH, Maleki F, Yousefikhoshbakht M, Haron H (2023). “Solving the vehicle routing problem with time windows using modified football game algorithm.” _Egyptian Informatics Journal_, *24*(4), 100403. ISSN 1110-8665, doi:[10.1016/j.eij.2023.100403](https://doi.org/10.1016/j.eij.2023.100403)
-- **Soccer: Soccer Games**: Purnomo HD, Wee H (2013). “Soccer Game Optimization.” In _Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance_, 386-420. IGI Global. doi:[10.4018/978-1-4666-2086-5.ch013](https://doi.org/10.4018/978-1-4666-2086-5.ch013)
-- **Soccer: Soccer Tournaments**: Osaba E, Diaz F, Onieva E (2014). “Golden ball: a novel meta-heuristic to solve combinatorial optimization problems based on soccer concepts.” _Applied Intelligence_, *41*(1), 145-166. doi:[10.1007/s10489-013-0512-y](https://doi.org/10.1007/s10489-013-0512-y)
-- **Soccer: Style**: Rashid MFFA (2020). “Tiki-taka algorithm: a novel metaheuristic inspired by football playing style.” _Engineering Computations_, *38*(1), 313-343. doi:[10.1108/ec-03-2020-0137](https://doi.org/10.1108/ec-03-2020-0137)
-- **Social Behavior**: Ray T, Liew K (2003). “Society and civilization: an optimization algorithm based on the simulation of social behavior.” _IEEE Transactions on Evolutionary Computation_, *7*(4), 386-396. doi:[10.1109/tevc.2003.814902](https://doi.org/10.1109/tevc.2003.814902)
-- **Social Behavior: Poor and Rich**: Moosavi SHS, Bardsiri VK (2019). “Poor and rich optimization algorithm: A new human-based and multi populations algorithm.” _Engineering Applications of Artificial Intelligence_, *86*, 165-181. doi:[10.1016/j.engappai.2019.08.025](https://doi.org/10.1016/j.engappai.2019.08.025)
-- **Social Behavior: Queuing**: Zhang J, Xiao M, Gao L, Pan Q (2018). “Queuing search algorithm: A novel metaheuristic algorithm for solving engineering optimization problems.” _Applied Mathematical Modelling_, *63*, 464-490.
-- **Social Behavior: Seeking**: Dai C, Chen W, Zhu Y (2010). “Seeker Optimization Algorithm for Digital IIR Filter Design.” _IEEE Transactions on Industrial Electronics_, *57*(5), 1710-1718. doi:[10.1109/tie.2009.2031194](https://doi.org/10.1109/tie.2009.2031194)
-- **Social Behavior: Thieves and Police**: Bagheri H, Ara AL, Hosseini R (2019). “Thieves and Police, a New Optimization Algorithm: Theory and Application in Probabilistic Power Flow.” _IETE Journal of Research_, 1-18. doi:[10.1080/03772063.2019.1672586](https://doi.org/10.1080/03772063.2019.1672586)
-- **Social Behavior: Urbanization**: Ghasemian H, Ghasemian F, Vahdat-Nejad H (2020). “Human urbanization algorithm: A novel metaheuristic approach.” _Mathematics and Computers in Simulation_, *178*, 1-15. doi:[10.1016/j.matcom.2020.05.023](https://doi.org/10.1016/j.matcom.2020.05.023)
-- **Social Engineering**: Fard AMF, Hajiaghaei-Keshteli M (2017). “Social Engineering Optimization (SEO); A New Single-Solution Meta-heuristic Inspired by Social Engineering.” In _International Conference on Industrial Engineering_.
-- **Social Spiders**: Cuevas E, Cienfuegos M, Zald\'\ivar D, Pérez-Cisneros M (2013). “A swarm optimization algorithm inspired in the behavior of the social-spider.” _Expert Systems with Applications_, *40*(16), 6374-6384. doi:[10.1016/j.eswa.2013.05.041](https://doi.org/10.1016/j.eswa.2013.05.041)
-- **Sonar**: Tzanetos A, Dounias G (2017). “A New Metaheuristic Method for Optimization: Sonar Inspired Optimization.” In Boracchi G, Iliadis L, Jayne C, Likas A (eds.), _Engineering Applications of Neural Networks_, 417-428. ISBN 978-3-319-65172-9.
-- **Sooty Tern**: Dhiman G, Kaur A (2019). “STOA: A bio-inspired based optimization algorithm for industrial engineering problems.” _Engineering Applications of Artificial Intelligence_, *82*, 148-174. doi:[10.1016/j.engappai.2019.03.021](https://doi.org/10.1016/j.engappai.2019.03.021)
-- **Sparrows: Desert Sparrows**: Sharma M, Sharma M, Sharma S (2021). “Desert sparrow optimization algorithm for the bicriteria flow shop scheduling problem with sequence-independent setup time.” _Operational Research_, *22*(4), 4353–4396. ISSN 1866-1505, doi:[10.1007/s12351-021-00675-w](https://doi.org/10.1007/s12351-021-00675-w)
-- **Sparrows: Genetic Sparrows**: Wu C, Fu X, Pei J, Dong Z (2021). “A Novel Sparrow Search Algorithm for the Traveling Salesman Problem.” _IEEE Access_, *9*, 153456–153471. ISSN 2169-3536, doi:[10.1109/access.2021.3128433](https://doi.org/10.1109/access.2021.3128433)
-- **Sparrows: Regular Sparrows**: Xue J, Shen B (2020). “A novel swarm intelligence optimization approach: sparrow search algorithm.” _Systems Science & Control Engineering_, *8*(1), 22–34. ISSN 2164-2583, doi:[10.1080/21642583.2019.1708830](https://doi.org/10.1080/21642583.2019.1708830)
-- **Sperm**: Raouf OA, Hezam IM (2017). “Sperm motility algorithm: a novel metaheuristic approach for global optimisation.” _International Journal of Operational Research_, *28*(2), 143. doi:[10.1504/ijor.2017.10002079](https://doi.org/10.1504/ijor.2017.10002079)
-- **Spirals**: Tamura K, and Keiichiro Yasuda (2011). “Spiral Dynamics Inspired Optimization.” _Journal of Advanced Computational Intelligence and Intelligent Informatics_, *15*(8), 1116-1122. doi:[10.20965/jaciii.2011.p1116](https://doi.org/10.20965/jaciii.2011.p1116)
-- **Sports Championships**: Kashan AH (2009). “League Championship Algorithm: A New Algorithm for Numerical Function Optimization.” In _2009 International Conference of Soft Computing and Pattern Recognition_. doi:[10.1109/socpar.2009.21](https://doi.org/10.1109/socpar.2009.21)
-- **Squirrels: Flying Squirrels**: Jain M, Singh V, Rani A (2018). “A novel nature-inspired algorithm for optimization: Squirrel search algorithm.” _Swarm and Evolutionary Computation_. doi:[10.1016/j.swevo.2018.02.013](https://doi.org/10.1016/j.swevo.2018.02.013)
-- **States of Matter**: Cuevas E, Reyna-Orta A, D\'\iaz-Cortes M (2017). “A Multimodal Optimization Algorithm Inspired by the States of Matter.” _Neural Processing Letters_, *48*(1), 517-556. doi:[10.1007/s11063-017-9750-z](https://doi.org/10.1007/s11063-017-9750-z)
-- **String Theory**: Rodriguez L, Castillo O, Garcia M, Soria J (2021). “A new meta-heuristic optimization algorithm based on a paradigm from physics: string theory.” _Journal of Intelligent & Fuzzy Systems_, *41*(1), 1657–1675. ISSN 1875-8967, doi:[10.3233/jifs-210459](https://doi.org/10.3233/jifs-210459)
-- **Swallows**: Neshat M, Sepidnam G, Sargolzaei M (2012). “Swallow swarm optimization algorithm: a new method to optimization.” _Neural Computing and Applications_, *23*(2), 429-454. doi:[10.1007/s00521-012-0939-9](https://doi.org/10.1007/s00521-012-0939-9)
-- **Symbiotic Organisms**: Cheng M, Prayogo D (2014). “Symbiotic Organisms Search: A new metaheuristic optimization algorithm.” _Computers & Structures_, *139*, 98-112. doi:[10.1016/j.compstruc.2014.03.007](https://doi.org/10.1016/j.compstruc.2014.03.007)
-
-### T
-- **Teachers**: Rao R, Savsani V, Vakharia D (2011). “Teaching—learning-based optimization: A novel method for constrained mechanical design optimization problems.” _Computer-Aided Design_, *43*(3), 303-315. doi:[10.1016/j.cad.2010.12.015](https://doi.org/10.1016/j.cad.2010.12.015)
-- **Termites: Alate**: Majumder A (2022). “Termite alate optimization algorithm: a swarm-based nature inspired algorithm for optimization problems.” _Evolutionary Intelligence_, *16*(3), 997–1017. ISSN 1864-5917, doi:[10.1007/s12065-022-00714-1](https://doi.org/10.1007/s12065-022-00714-1)
-- **Termites: Colonies**: Hedayatzadeh R, Salmassi FA, Keshtgari M, Akbari R, Ziarati K (2010). “Termite colony optimization: A novel approach for optimizing continuous problems.” In _2010 18th Iranian Conference on Electrical Engineering_. doi:[10.1109/iraniancee.2010.5507009](https://doi.org/10.1109/iraniancee.2010.5507009)
-- **Time Travel**: Fedor B, Straub J (2022). “A Particle Swarm Optimization Backtracking Technique Inspired by Science-Fiction Time Travel.” _AI_, *3*(2), 390–415. ISSN 2673-2688, doi:[10.3390/ai3020024](https://doi.org/10.3390/ai3020024)
-- **Tree Growth**: Mahmoodabadi MJ, Rasekh M, Yahyapour M (2022). “Tree optimization algorithm (TOA): a novel metaheuristic approach for solving mathematical test functions and engineering problems.” _Evolutionary Intelligence_, *16*(4), 1325–1338. ISSN 1864-5917, doi:[10.1007/s12065-022-00742-x](https://doi.org/10.1007/s12065-022-00742-x)
-- **Troops of Soldiers**: Chen T (2009). “A Simulative Bionic Intelligent Optimization Algorithm: Artificial Searching Swarm Algorithm and Its Performance Analysis.” In _2009 International Joint Conference on Computational Sciences and Optimization_. doi:[10.1109/cso.2009.183](https://doi.org/10.1109/cso.2009.183)
-- **Tug of War**: Kaveh A, Zolghadr A (2016). “A novel meta-heuristic algorithm: tug of war optimization.” _Iran University of Science & Technology_, *6*(4), 469-492.
-- **Turnicates**: Kaur S, Awasthi LK, Sangal A, Dhiman G (2020). “Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization.” _Engineering Applications of Artificial Intelligence_, *90*, 103541. doi:[10.1016/j.engappai.2020.103541](https://doi.org/10.1016/j.engappai.2020.103541)
-
-### U
-
-### V
-- **Vaccination**: Tayeb FB, Bessedik M, Benbouzid M, Cheurfi H, Blizak A (2017). “Research on Permutation Flow-shop Scheduling Problem based on Improved Genetic Immune Algorithm with vaccinated offspring.” _Procedia Computer Science_, *112*, 427-436. doi:[10.1016/j.procs.2017.08.055](https://doi.org/10.1016/j.procs.2017.08.055)
-- **Vehicles**: Savsani P, Savsani V (2016). “Passing vehicle search (PVS): A novel metaheuristic algorithm.” _Applied Mathematical Modelling_, *40*(5-6), 3951-3978. doi:[10.1016/j.apm.2015.10.040](https://doi.org/10.1016/j.apm.2015.10.040)
-- **Vibrating Particles**: Kaveh A, Ghazaan MI (2016). “Vibrating particles system algorithm for truss optimization with multiple natural frequency constraints.” _Acta Mechanica_, *228*(1), 307-322. doi:[10.1007/s00707-016-1725-z](https://doi.org/10.1007/s00707-016-1725-z)
-- **Virus: Attacking**: Liang Y, Juarez JRC (2015). “A novel metaheuristic for continuous optimization problems: Virus optimization algorithm.” _Engineering Optimization_, *48*(1), 73-93. doi:[10.1080/0305215x.2014.994868](https://doi.org/10.1080/0305215x.2014.994868)
-- **Virus: Swine Flu**: Pattnaik S, Bakwad K, Sohi B, Ratho R, Devi S (2013). “Swine Influenza Models Based Optimization (SIMBO).” _Applied Soft Computing_, *13*(1), 628-653. doi:[10.1016/j.asoc.2012.07.010](https://doi.org/10.1016/j.asoc.2012.07.010)
-- **Viruses: Virulence**: Jaderyan M, Khotanlou H (2016). “Virulence Optimization Algorithm.” _Applied Soft Computing_, *43*, 596-618. doi:[10.1016/j.asoc.2016.02.038](https://doi.org/10.1016/j.asoc.2016.02.038)
-- **Viruses: Virus Colonies**: Li MD, Zhao H, Weng XW, Han T (2016). “A novel nature-inspired algorithm for optimization: Virus colony search.” _Advances in Engineering Software_, *92*, 65-88. doi:[10.1016/j.advengsoft.2015.11.004](https://doi.org/10.1016/j.advengsoft.2015.11.004)
-- **Viruses: Virus Replication**: Cortés P, Garc\'\ia JM, Muñuzuri J, Onieva L (2008). “Viral systems: A new bio-inspired optimisation approach.” _Computers & Operations Research_, *35*(9), 2840-2860. doi:[10.1016/j.cor.2006.12.018](https://doi.org/10.1016/j.cor.2006.12.018)
-- **Volleyball Leagues**: Moghdani R, Salimifard K (2018). “Volleyball Premier League Algorithm.” _Applied Soft Computing_, *64*, 161-185. doi:[10.1016/j.asoc.2017.11.043](https://doi.org/10.1016/j.asoc.2017.11.043)
-- **Vortices**: Doğan B, Ölmez T (2015). “A new metaheuristic for numerical function optimization: Vortex Search algorithm.” _Information Sciences_, *293*, 125-145. doi:[10.1016/j.ins.2014.08.053](https://doi.org/10.1016/j.ins.2014.08.053)
-- **Vultures: Africans**: Abdollahzadeh B, Gharehchopogh FS, Mirjalili S (2021). “African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems.” _Computers & Industrial Engineering_, *158*, 107408. ISSN 0360-8352, doi:[10.1016/j.cie.2021.107408](https://doi.org/10.1016/j.cie.2021.107408)
-- **Vultures: Egyptian**: Sur C, Sharma S, Shukla A (2013). “Egyptian Vulture Optimization Algorithm — A New Nature Inspired Meta-heuristics for Knapsack Problem.” In _The 9th International Conference on Computing and InformationTechnology (IC2IT2013)_, 227-237. Springer Berlin Heidelberg. doi:[10.1007/978-3-642-37371-8_26](https://doi.org/10.1007/978-3-642-37371-8_26)
-
-### W
-- **Walruses**: Trojovský P, Dehghani M (2023). “A new bio-inspired metaheuristic algorithm for solving optimization problems based on walruses behavior.” _Scientific Reports_, *13*(1). ISSN 2045-2322, doi:[10.1038/s41598-023-35863-5](https://doi.org/10.1038/s41598-023-35863-5)
-- **Wasps**: Pinto P, Runkler TA, Sousa JM (2005). “Wasp swarm optimization of logistic systems.” In _Adaptive and Natural Computing Algorithms_, 264-267. Springer.
-- **Water: Hydrological Cycle**: Wedyan A, Whalley J, Narayanan A (2017). “Hydrological Cycle Algorithm for Continuous Optimization Problems.” _Journal of Optimization_, *2017*, 1-25. doi:[10.1155/2017/3828420](https://doi.org/10.1155/2017/3828420)
-- **Water: Hydrological Cycle Bis**: Yan X, Niu B (2018). “Hydrologic Cycle Optimization Part I: Background and Theory.” In _Advances in Swarm Intelligence_, 341–349. ISBN 9783319938158, doi:[10.1007/978-3-319-93815-8_33](https://doi.org/10.1007/978-3-319-93815-8_33)
-- **Water: Intelligent Water Drops**: Hosseini HS (2009). “The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm.” _International Journal of Bio-Inspired Computation_, *1*(1/2), 71. doi:[10.1504/ijbic.2009.022775](https://doi.org/10.1504/ijbic.2009.022775)
-- **Water: Rain**: Kaboli SHA, Selvaraj J, Rahim N (2017). “Rain-fall optimization algorithm: A population based algorithm for solving constrained optimization problems.” _Journal of Computational Science_, *19*, 31-42. doi:[10.1016/j.jocs.2016.12.010](https://doi.org/10.1016/j.jocs.2016.12.010)
-- **Water: Rain Drops**: Jiang Q, Wang L, Hei X, Fei R, Yang D, Zou F, Li H, Cao Z, Lin Y (2014). “Optimal approximation of stable linear systems with a novel and efficient optimization algorithm.” In _2014 IEEE Congress on Evolutionary Computation (CEC)_. doi:[10.1109/cec.2014.6900366](https://doi.org/10.1109/cec.2014.6900366)
-- **Water: Water Cycle**: Eskandar H, Sadollah A, Bahreininejad A, Hamdi M (2012). “Water cycle algorithm — A novel metaheuristic optimization method for solving constrained engineering optimization problems.” _Computers & Structures_, *110-111*, 151-166. doi:[10.1016/j.compstruc.2012.07.010](https://doi.org/10.1016/j.compstruc.2012.07.010)
-- **Water: Water Evaporation**: Kaveh A, Bakhshpoori T (2016). “Water Evaporation Optimization: A novel physically inspired optimization algorithm.” _Computers & Structures_, *167*, 69-85. doi:[10.1016/j.compstruc.2016.01.008](https://doi.org/10.1016/j.compstruc.2016.01.008)
-- **Water: Water Flow**: Tran TH, Ng KM (2010). “A water-flow algorithm for flexible flow shop scheduling with~intermediate buffers.” _Journal of Scheduling_, *14*(5), 483-500. doi:[10.1007/s10951-010-0205-x](https://doi.org/10.1007/s10951-010-0205-x)
-- **Water: Water Molecules**: Daliri A, Asghari A, Azgomi H, Alimoradi M (2022). “The water optimization algorithm: a novel metaheuristic for solving optimization problems.” _Applied Intelligence_, *52*(15), 17990–18029. ISSN 1573-7497, doi:[10.1007/s10489-022-03397-4](https://doi.org/10.1007/s10489-022-03397-4)
-- **Water: Water Wave**: Zheng Y (2015). “Water wave optimization: A new nature-inspired metaheuristic.” _Computers & Operations Research_, *55*, 1-11. doi:[10.1016/j.cor.2014.10.008](https://doi.org/10.1016/j.cor.2014.10.008)
-- **Whales: Beluga**: Zhong C, Li G, Meng Z (2022). “Beluga whale optimization: A novel nature-inspired metaheuristic algorithm.” _Knowledge-Based Systems_, *251*, 109215. ISSN 0950-7051, doi:[10.1016/j.knosys.2022.109215](https://doi.org/10.1016/j.knosys.2022.109215)
-- **Whales: Binary Whales**: K. SR, Panwar L, Panigrahi BK, Kumar R (2018). “Binary whale optimization algorithm: a new metaheuristic approach for profit-based unit commitment problems in competitive electricity markets.” _Engineering Optimization_, *51*(3), 369-389. doi:[10.1080/0305215x.2018.1463527](https://doi.org/10.1080/0305215x.2018.1463527)
-- **Whales: Killer Whales**: Biyanto TR, Matradji, Irawan S, Febrianto HY, Afdanny N, Rahman AH, Gunawan KS, Pratama JA, Bethiana TN (2017). “Killer Whale Algorithm: An Algorithm Inspired by the Life of Killer Whale.” _Procedia Computer Science_, *124*, 151-157. doi:[10.1016/j.procs.2017.12.141](https://doi.org/10.1016/j.procs.2017.12.141)
-- **Whales: Orcas**: Drias H, Drias Y, Khennak I (2020). “A New Swarm Algorithm Based on Orcas Intelligence for Solving Maze Problems.” In _Trends and Innovations in Information Systems and Technologies_, 788-797. Springer International Publishing. doi:[10.1007/978-3-030-45688-7_77](https://doi.org/10.1007/978-3-030-45688-7_77)
-- **Whales: Regular Whales**: Mirjalili S, Lewis A (2016). “The Whale Optimization Algorithm.” _Advances in Engineering Software_, *95*, 51-67. doi:[10.1016/j.advengsoft.2016.01.008](https://doi.org/10.1016/j.advengsoft.2016.01.008)
-- **Whales: Sperm Whales**: Ebrahimi A, Khamehchi E (2016). “Sperm whale algorithm: An effective metaheuristic algorithm for production optimization problems.” _Journal of Natural Gas Science and Engineering_, *29*, 211-222. doi:[10.1016/j.jngse.2016.01.001](https://doi.org/10.1016/j.jngse.2016.01.001)
-- **Whirlpools**: Ghasemi M, Davoudkhani IF, Akbari E, Rahimnejad A, Ghavidel S, Li L (2020). “A novel and effective optimization algorithm for global optimization and its engineering applications: Turbulent Flow of Water-based Optimization (TFWO).” _Engineering Applications of Artificial Intelligence_, *92*, 103666. doi:[10.1016/j.engappai.2020.103666](https://doi.org/10.1016/j.engappai.2020.103666)
-- **Wildebeests**: Amali DGB, Dinakaran M (2019). “Wildebeest herd optimization: A new global optimization algorithm inspired by wildebeest herding behaviour.” _Journal of Intelligent & Fuzzy Systems_, *37*(6), 8063–8076. ISSN 1875-8967, doi:[10.3233/jifs-190495](https://doi.org/10.3233/jifs-190495)
-- **Wind**: Bayraktar Z, Komurcu M, Werner DH (2010). “Wind Driven Optimization (WDO): A novel nature-inspired optimization algorithm and its application to electromagnetics.” In _2010 IEEE Antennas and Propagation Society International Symposium_. doi:[10.1109/aps.2010.5562213](https://doi.org/10.1109/aps.2010.5562213)
-- **Wingsuit**: Yang J, Zhang Y, Wang Z, Todo Y, Lu B, Gao S (2021). “A Cooperative Coevolution Wingsuit Flying Search Algorithm with Spherical Evolution.” _International Journal of Computational Intelligence Systems_, *14*(1). ISSN 1875-6883, doi:[10.1007/s44196-021-00030-z](https://doi.org/10.1007/s44196-021-00030-z)
-- **Wolves: Grey Wolves**: Mirjalili S, Mirjalili SM, Lewis A (2014). “Grey Wolf Optimizer.” _Advances in Engineering Software_, *69*, 46-61. doi:[10.1016/j.advengsoft.2013.12.007](https://doi.org/10.1016/j.advengsoft.2013.12.007)
-- **Wolves: Wolf Packs**: Yang C, Tu X, Chen J (2007). “Algorithm of Marriage in Honey Bees Optimization Based on the Wolf Pack Search.” In _The 2007 International Conference on Intelligent Pervasive Computing (IPC 2007)_. doi:[10.1109/ipc.2007.104](https://doi.org/10.1109/ipc.2007.104)
-- **Wolves: Wolves**: Tang R, Fong S, Yang X, Deb S (2012). “Wolf search algorithm with ephemeral memory.” In _Seventh International Conference on Digital Information Management (ICDIM 2012)_. doi:[10.1109/icdim.2012.6360147](https://doi.org/10.1109/icdim.2012.6360147)
-- **Worms**: Arnaout J (2014). “Worm optimization: a novel optimization algorithm inspired by C. Elegans.” In _Proceedings of the 2014 International Conference on Industrial Engineering and Operations Management_, 2499-2505.
-
-### X
-
-### Y
-- **Yellow Saddle Goatfish**: Zald\'\ivar D, Morales B, Rodr\'\iguez A, Valdivia-G A, Cuevas E, Pérez-Cisneros M (2018). “A novel bio-inspired optimization model based on Yellow Saddle Goatfish behavior.” _Biosystems_, *174*, 1-21. doi:[10.1016/j.biosystems.2018.09.007](https://doi.org/10.1016/j.biosystems.2018.09.007)
-- **Yin-Yang Pairs**: Punnathanam V, Kotecha P (2016). “Yin-Yang-pair Optimization: A novel lightweight optimization algorithm.” _Engineering Applications of Artificial Intelligence_, *54*, 62-79. doi:[10.1016/j.engappai.2016.04.004](https://doi.org/10.1016/j.engappai.2016.04.004)
-
-### Z
-- **Zebras**: Trojovska E, Dehghani M, Trojovsky P (2022). “Zebra Optimization Algorithm: A New Bio-Inspired Optimization Algorithm for Solving Optimization Algorithm.” _IEEE Access_, *10*, 49445–49473. ISSN 2169-3536, doi:[10.1109/access.2022.3172789](https://doi.org/10.1109/access.2022.3172789)
-- **Zombies**: Nguyen HT, Bhanu B (2012). “Zombie Survival Optimization: A swarm intelligence algorithm inspired by zombie foraging.” In _Pattern Recognition (ICPR), 2012 21st International Conference on_, 987-990. IEEE.
-
-***
-
-### Maintainers
-("the Zoo Keepers")
-
-- [Claus Aranha](mailto:caranha@cs.tsukuba.ac.jp), Tsukuba University, Japan.
-- [Felipe Campelo](mailto:fcampelo@ufmg.br), Universidade Federal de Minas Gerais (UFMG), Brazil.
-
-### Contributors
-(at least one contribution to the bestiary - in terms of adding a method to the list, not inventing it!)
-
-- Adré Steyn - University of Stellenbosch, South Africa
-- Alberto Franzin - Université Libre de Bruxelles, Belgium
-- Alexander J. Benavides - Universidad Nacional de San Agustín de Arequipa (UNSA), Perú
-- Alexander Magazinov, Tel Aviv University, Israel
-- Anand Subramanian - UFPB, Brazil
-- André Maravilha - UFMG, Brazil
-- Carlos Fonseca - University of Coimbra, Portugal
-- Ciniro Nametala - UFMG, Brazil
-- Christian L. Camacho Villalón - ULB, Brussels
-- Daniel Palhazi Cuervo - Ascenium, Norway
-- Denis Pallez - University Côte d'Azur, France
-- Eduardo Hauck - UFJF, Brazil
-- Fabio Daolio - University of Stirling, Scotland UK
-- Fernanda Takahashi - UFMG, Brazil
-- Fernando Otero - University of Kent, England UK
-- Fillipe Goulart - UFMG, Brazil
-- Federico Pagnozzi - Université Libre de Bruxelles, Belgium
-- Krystian Lapa - Institute of Computational Intelligence, Poland
-- Iago Augusto de Carvalho - Universidade Federal de Alfenas, Brazil
-- Iztok Fister Jr. - University of Maribor, Slovenia
-- Jakub Grabski - Poznan University of Technology, Poland
-- James Brookhouse - University of Kent, England UK
-- James McDermott - University of Galway, Ireland
-- Juan Carlos Chacon-Hurtado - TU Delft, Netherlands
-- Joao Duro - University of Sheffield, England UK
-- Joaquín Antonio Pacheco Bonrostro - University of Burgos, Spain
-- Kenneth Sörensen - University of Antwerp, Belgium
-- Konstantinos Zervoudakis - Technical University of Crete, Greece
-- Koen van der Blom - Leiden University, Netherlands
-- Lars Magnus Hvattum - Molde University College, Norway
-- Leandro Santos Coelho - UFPR, Brazil
-- Leonardo Goliatt da Fonseca - Universidade Federal de Juiz de Fora, Brazil
-- Luís Correia - Universidade de Lisboa, Portugal
-- Marcelo Maia - Federal University of Uberlândia, Brazil
-- Marco Mollinetti - University of Tsukuba, Japan
-- Marco Pranzo - Università di Siena, Italy
-- Marcus Ritt - UFRGS, Brazil
-- Marc Sevaux - Université Bretagne-Sud, France
-- Michał Okulewicz - Politechnika Warszawska, Poland
-- Michael Lones - Heriot-Watt University, Scotland
-- Nadarajen Veerapen - University of Stirling, Scotland UK
-- Nguyen Tri Hai - Chung-Ang University, South Korea
-- Nuno Rodrigues - University of Lisbon, Lisbon
-- Owein Thuillier - Université Bretagne-Sud, France
-- Paul Rubin - Michigan State University, USA
-- Peter Lewis - Aston University, UK
-- Pieter Leyman - Ghent University, Belgium
-- Rafael Stubs Parpinelli - Universidade do Estado de Santa Catarina (UDESC), Brasil
-- Robin Purshouse - University of Sheffield, England UK
-- Romain Billot - IMT Atlantique, France
-- Rubén Ruiz - Universitat Politècnica de València, Spain
-- Ruud Koot - Universiteit Utrecht, The Netherlands
-- Sara Silva - University of Lisbon
-- Sander - Leiden University, Netherlands
-- senorramirez
-- Sergio A. Rojas - Universidad Distrital de Bogotá, Colombia
-- Silvano Martello - University of Bologna, Italy
-- Stefan Voß - Universität Hamburg, Germany
-- Thomas Jacob Riis Stidsen - Danmarks Tekniske Universitet, Denmark
-- Thomas Stützle - Université Libre de Bruxelles, Belgium
-- Tushar Semwal - IIT Guwahati, India
-- Yuri Lavinas - University of Tsukuba, Brazil
-
-***
-
-### How to Contribute
-
-If you know a paper that should belong to this list, please send an
-e-mail to either [Claus](mailto:caranha@cs.tsukuba.ac.jp) or [Felipe](mailto:fcampelo@ufmg.br), or report an issue on our [Github repo](https://github.com/fcampelo/EC-bestiary). The criteria for inclusion are quite simple:
-
-1. the work must be in a peer reviewed publication (journal or conference);
-2. the title or abstract must name the algorithm after the natural (or supernatural) metaphor on which it was based;
-
-It is also important to highlight that only the earliest known mention for each metaphor is included.
-
-### More Info:
-- Some of the algorithms listed here were found in a list compiled by Iztok Fister Jr. _et al._, which is available [here](http://www.iztok-jr-fister.eu/static/publications/21.pdf). Iztok also recently published [this paper](http://www.iztok-jr-fister.eu/static/publications/Stu2016.pdf) reflecting on the proliferation of metaphors in EC research.
-- A fantastic parody of this whole metaphor craze can be read [here](http://www.oneweirdkerneltrick.com/spectral.pdf). Highly recommended!
-
-### License:
-This work is licensed under the Creative Commons CC BY-NC-SA 4.0 license (Attribution Non-Commercial Share Alike International License version 4.0): [http://creativecommons.org/licenses/by-nc-sa/4.0/](http://creativecommons.org/licenses/by-nc-sa/4.0/)
diff --git a/README_maker/README_Maintainers_and_Contributors.md b/README_maker/README_Maintainers_and_Contributors.md
index 964a437..2e1ca46 100644
--- a/README_maker/README_Maintainers_and_Contributors.md
+++ b/README_maker/README_Maintainers_and_Contributors.md
@@ -7,6 +7,7 @@
### Contributors
(at least one contribution to the bestiary - in terms of adding a method to the list, not inventing it!)
+- Aaron T. Becker - University of Houston, USA
- Adré Steyn - University of Stellenbosch, South Africa
- Alberto Franzin - Université Libre de Bruxelles, Belgium
- Alexander J. Benavides - Universidad Nacional de San Agustín de Arequipa (UNSA), Perú