This repository contains several implementations and experimental analyses of metaheuristic optimization algorithms, focusing on their behavior on benchmark numerical functions and combinatorial optimization problems.
The projects explore and compare different approaches such as Genetic Algorithms, Hill Climbing and Simulated Annealing, evaluating their convergence, robustness and performance on complex search landscapes.
The following studies are included:
-
Genetic Algorithms vs Hill Climbing & Simulated Annealing
Performance comparison on benchmark functions such as De Jong, Schwefel, Rastrigin and Michalewicz, across multiple dimensions. -
Hill Climbing Optimization Analysis
Study of Best Improvement and First Improvement strategies on a discrete unimodal function. -
Enhanced Genetic Algorithm
Improved GA using adaptive operators and hybrid local search to improve convergence and solution quality. -
Memetic Algorithm for the Quadratic Assignment Problem (QAP)
Hybrid approach combining Genetic Algorithms with Simulated Annealing for solving large combinatorial optimization problems.
C++ β’ Metaheuristic Algorithms β’ Optimization β’ Benchmark Functions