Title: A hybrid ACO/PSO algorithm and its applications

Authors: Yi Zhang, Meng Zhang, Yan-chun Liang

Addresses: Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China; Department of Computer Science, Jilin Business and Technology College, Changchun 130062, China. ' Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China. ' Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China

Abstract: In this paper, we present a hybrid optimisation algorithm with particle swarm optimisation (PSO) and ant colony optimisation (ACO). Unlike the conventional ACO or PSO, the new optimisation scheme makes full use of the attributes of both algorithms. In the proposed algorithm, the PSO is used to optimise the parameters in the ACO, which means that the selection of parameters does not depend on artificial experiences or trial and error, but relies on the adaptive search of the particles in the PSO. We also make an optimised implementation of ACO, by which the running time of ants routing is largely reduced. The results of the simulated experiments show that the improved algorithm not only reduces the number of routes in the ACO, but also surpasses existing algorithms in performance for solving large-scale TSP problems. Simulation results also show that the speed of convergence of ACO algorithm could be enhanced greatly.

Keywords: ant colony optimisation; ACO; particle swarm optimisation; PSO; travelling salesman problem; TSP; parallel strategy; self-adaptive; hybrid optimisation; simulation.

DOI: 10.1504/IJMIC.2009.030077

International Journal of Modelling, Identification and Control, 2009 Vol.8 No.4, pp.309 - 316

Available online: 09 Dec 2009 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article