Hybrid ant colony optimization algorithm for solving the traveling salesman problem

Abstract

Abstract The Traveling Salesman Problem (TSP) is a classical NP-hard combinatorial optimization challenge with significant theoretical and practical implications. This paper proposes a Hybrid Ant Colony Optimization algorithm (HACO) that integrates 2-opt local search with an adaptive pheromone evaporation mechanism. Experimental results on TSPLIB benchmarks demonstrate that HACO outperforms basic AS and ACS, achieving 0.04% average relative error on ch150 instances. Comprehensive comparisons with six state-of-the-art methods confirm HACO’s superior convergence speed (9.2% faster than ACO-DE).