A New Ant Supervised-PSO Variant Applied to Traveling Salesman Problem

Abstract

The Traveling Salesman Problem (TSP) is one of the standard test problems often used for benchmarking of discrete optimization algorithms. Several meta-heuristic methods, including ant colony optimization (ACO), particle swarm optimization (PSO), bat algorithm, and others, were applied to the TSP in the past. Hybrid methods are generally composed of several optimization algorithms. Ant Supervised by Particle Swarm Optimization (AS-PSO) is a hybrid schema where ACO plays the role of the main optimization procedure and PSO is used to detect optimum values of ACO parameters α, β, the amount of pheromones \( {\mathcal{T}} \) and evaporation rate ρ. The parameters are applied to the ACO algorithm which is used to search for good paths between the cities. In this paper, an Extended AS-PSO variant is proposed. In addition to the previous version, it allows to optimize the parameter, \( {\mathcal{T}} \) and the parameter, ρ. The effectiveness of the proposed method is evaluated on a set of well-known TSP problems. The experimental results show that both the average solution and the percentage deviation of the average solution to the best known solution of the proposed method are better than others methods.