Ant-Colony-System

Based on the paper M. Dorigo and L. M. Gambardella, "Ant colony system: a cooperative learning approach to the traveling salesman problem," in IEEE Transactions on Evolutionary Computation, vol. 1, no. 1, pp. 53-66, April 1997, doi: 10.1109/4235.585892.

Motivation

Understand the algorithm: Code was writted to illustrate / understand the algorithms steps instead of efficient code.

Usage

#include "ACO/ACO.h"

To create nodes:
' ACO::NodeManager::SetParamsAndMakeNodes(total_nodes, minX, maxX, minY, maxY);'

To run a simluation with the nodes :

ACO::AntSystem::SimSettings ant_system_default_settings;  
ACO::AntSystem::AntSystem ant_system(ant_system_default_settings);  
float AntSystem_best = ant_system.Run(ACO::NodeManager::GetNodes(), iterations, total_ants);  

ACO::AntColonySystem::SimSettings ant_colony_system_default_settings;  
ACO::AntColonySystem::AntColonySystem ant_colony_system(ant_colony_system_default_settings);  
float AntColonysystem_best = ant_colony_system.Run(ACO::NodeManager::GetNodes(), iterations, total_ants);  

Class: AntSystem:

The original algorithm discussed in the background section of the paper. Based on M. Dorigo, Optimization learning and natural algorithms, 1992.

Class: AntColonySystem

The proposed algorithm in this paper.

Required Libraries:

• Used Effolkronium for random numbers: https://github.com/effolkronium/random
• if a custom random function is used updated the base.h and relevant "random::get<>".

General Nodes

• Currently uses randomly generated node locations.
• All nodes are fully connected.
• Ant start positions are random.
• C++ 17