Parameter adaptation-based ant colony optimization with dynamic hybrid mechanism

Abstract

In this paper, a parameter adaptation-based ant colony optimization (ACO) algorithm based on particle swarm optimization (PSO) algorithm with the global optimization ability, fuzzy system with the fuzzy reasoning ability and 3-Opt algorithm with local search ability, namely PF3SACO is proposed to improve the optimization ability and convergence, avoid to fall into local optimum. In the PF3SACO, a new dynamic parameter adjustment mechanism by the PSO and the fuzzy system is designed to adaptively adjust the pheromone importance factor α, pheromone volatilization coefficient ρ and the heuristic function importance factor β to accelerate the convergence, improve the search ability, enhance the local search ability and avoid premature. This is achievable by parameter adaptation to reflect the dynamic search characteristic by exploring and exploiting in the search process for the parameter values to be close to the optimal values. In addition, 3-Opt algorithm is applied to optimize the generated path to eliminate the cross path, obtain the optimal path and avoid to fall into local optimum. The optimization performance of the PF3SACO is investigated on fifteen travelling salesman problems (TSPs) with the scales from 42 to 783 cities. The experiment results show that the PF3SACO has better optimization performance by comparing with ABC, NACO, HYBRID, ACO-3Opt, PACO-3Opt, PSO-ACO-3Opt and some other well-known algorithms in most TSP in term of the solution quality, robustness and space distribution. It provides a reference to solve the large-scale TSP for obtaining better path length.