ACO Framework

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1. Introduction:

ACO Framework is created for "Inteligencja Obliczeniowa" course on University of Science and Technology in Cracow.
Created to effective solve Ant Colony Optimization problems in Scala. It's also prepared for multi objective optimization.

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2. Implemented solutions:

Currently, there are implemented algorithms for:

1. Single Objective Ant Colony Optimization named Tsp for Travelling Salesman Problem
2. Multi Objective Ant Colony Optimization maned Mtsp also for TSP kind of problems

Architecture Diagram:

All abstract classes are named with pattern `Base___.scala`, those classes inform user about functions that must be implemented to run algorithm.

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3. Usage:

Needed steps:

1. Use config.yaml file to determine problem files. Example:

problemType: mtsp
problemFiles:
  - mtsp//dimacs_15_a.tsp <br>
  - mtsp//dimacs_15_b.tsp <br>
  - mtsp//dimacs_15_c.tsp <br>

Where:

• problemType - type of optimization problem (currently available tsp or mtsp)
• problemFiles - input problem files

2. Used algorithm define all remaining parameters. Quick description:

• increment - defines value that will be added to pheromone table when ant pass selected edge
• alpha - power used in pheromone calculation as
• beta - power used in distance heuristic as
• extinction - percentage loss of pheromone value after each algorithm iteration
• distanceWeights - weights used to flatten distances in multi objective calculations, should sum up to 1
• pheromoneWeights - weights used to flatten pheromone values, should sum up to 1
• antNumb - number of ants used for optimization (declared in Main.scala)
• iteration - number of iterations that algorithm can evaluate (declared in Main.scala)
• rnd - random number generator (you can set seed to create more deterministic results)
• takenAntsToPheromoneUpdate - used only in TSP problems, determine how many ants update pheromone table (based on fitness value)

3. Run Main.scala to run algorithm, results will be printed in console. This file only read the selected problem and map it into BaseProblem instance, that can be further evaluated.

4. Prepared solutions offers algorithms:

• weighted sum - for distance and pheromone flattening
• tbd

5. Included problems:

• Berlin 52 - dataset with 52 dimensions (locations from Berlin) from here.
• DIMACS 15 - dataset with 15 dimensions in 3 different objectives (a, b, c), you can find it here
• Lust 100 - dataset with 100 dimensions in 2 objectives (A, B), also from here
• Paquete 100 - dataset with 100 dimensions in 2 objectives (A, B) here

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4. Results:

TSP

Testing results:
On included into Framework dataset Berlin 52, we performed test run. Here are the results:
Data set visualization:

Selected parameters:

Parameter	Value
increment	5
alpha	1
beta	1
extinction	0.2
distanceWeights	1.0
pheromoneWeights	1.0
takenAntsToPheromoneUpdate	30
ants_number	30
algorithm_iterations	100

Results:

Best Fitness: 8356.66
NOTE Currently there is no visualization methods in Framework!

MTSP

On included into Framework dataset portgen-100-101(paquete_euclidAB100) and lust_kroA100. We performed test run. Here are the results

Selected parameters:

Parameter	Value
increment	5
alpha	1
beta	1
extinction	0.2
distanceWeights	[0.5,0.5]
pheromoneWeights	[0.5,0.5]
takenAntsToPheromoneUpdate	100
ants_number	100
algorithm_iterations	100

Results:

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5. Possible Further Work:

• Adding More Algorithms to solve different problems
• Adding Visualization Methods (e.g. Front Pareto)

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Authors:

Krzysztof Tylka-Suleja Email: tylkasuleja@student.agh.edu.pl
Tymoteusz Dobrzański Email: tdobrzanski@student.agh.edu.pl

In cooperation with:

Aleksander Byrski Email: olekb@agh.edu.pl
Mateusz Starzec Email: starzec.mateusz@gmail.com
Grażyna Starzec Email: gstarzec@agh.edu.pl