Ant Colony Optimization for a 50 City Traveling Salesman Problem
A Parameterized Benchmark Study with Convergence Analysis
DOI:
https://doi.org/10.61704/pr.524Keywords:
Traveling Salesman Problem; ant colony optimization; metaheuristics; swarm intelligence; benchmarking; convergence analysisAbstract
This paper evaluates Ant Colony Optimization (ACO) on a mid-scale Traveling Salesman Problem (TSP) with 50 cities. We present a complete problem formulation, a parameterized algorithmic design, and a compact experimental protocol that emphasizes interpretability via full methodological detail and a fully specified coordinate set. Two figures visualize the best route and the best-so-far convergence trajectory. With a standard ACO configuration (α=1, β=5, ρ=0.5), the best tour length is 566.75 distance units, an 18.4% improvement over a Nearest-Neighbor baseline (694.76). We analyze convergence behavior, sensitivity to key parameters, computational cost, and practical implications for early-stopping and reproducible benchmarking. The study positions these results within the TSP/ACO literature and offers concise recommendations for fair, mid-scale comparisons.
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Copyright (c) 2025 Ali A. Al-Arbo, Younis A. Al-Arbo
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Copyright © 2025 by the authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). You may not alter or transform this work in any way without permission from the authors. Non-commercial use, distribution, and copying are permitted, provided that appropriate credit is given to the authors and Al-Hadba University.
