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Ant Colony Optimization in Julia

License: MIT License

Julia 100.00%
ant-colony-optimization julia ant-colony-algorithm julia-language swarm-intelligence swarm-intelligence-algorithms swarm-optimization-algorithm traveling-salesman traveling-salesman-problem traveling-salesperson

ants's Introduction

Ants

Ant Colony Optimization (ACO) algorithm for Traveling Salesman Problem (TSP) implemented in Julia.

Usage: Distance Matrices

Description

Input data for TSP can be defined in a form of a distance matrix. The matrix can be defined as a Julia array or loaded from a file.

Matrix must have Inf as a value for non-existing edges.

Example

Results for D1 matrix from data/dists.jl.

D1

Usage: TSPLIB

Description

Datasets from TSPLIB database are also supported.

Example

Optimization was performed on Wi29 dataset.

During the experiments the suboptimal tour of length 27760.0 was found in just 100 iterations with 50 ants.

Wi29My

The optimal tour of length 27603.0 is illustrated below.

Wi29Opt

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