Applied Intelligence

, Volume 34, Issue 1, pp 64–73

Study on hybrid PS-ACO algorithm

Authors

    • National Key Laboratory of Nano/Micro Fabrication Technology, Key laboratory for Thin Film and Microfabrication of Ministry of Education, Research Institute of Micro/Nano Science and TechnologyShanghai Jiao Tong University
  • Jiapin Chen
    • National Key Laboratory of Nano/Micro Fabrication Technology, Key laboratory for Thin Film and Microfabrication of Ministry of Education, Research Institute of Micro/Nano Science and TechnologyShanghai Jiao Tong University
  • Zhenbo Li
    • National Key Laboratory of Nano/Micro Fabrication Technology, Key laboratory for Thin Film and Microfabrication of Ministry of Education, Research Institute of Micro/Nano Science and TechnologyShanghai Jiao Tong University
Article

DOI: 10.1007/s10489-009-0179-6

Cite this article as:
Shuang, B., Chen, J. & Li, Z. Appl Intell (2011) 34: 64. doi:10.1007/s10489-009-0179-6

Abstract

Ant colony optimization (ACO) algorithm is a recent meta-heuristic method inspired by the behavior of real ant colonies. The algorithm uses parallel computation mechanism and performs strong robustness, but it faces the limitations of stagnation and premature convergence. In this paper, a hybrid PS-ACO algorithm, ACO algorithm modified by particle swarm optimization (PSO) algorithm, is presented. The pheromone updating rules of ACO are combined with the local and global search mechanisms of PSO. On one hand, the search space is expanded by the local exploration; on the other hand, the search process is directed by the global experience. The local and global search mechanisms are combined stochastically to balance the exploration and the exploitation, so that the search efficiency can be improved. The convergence analysis and parameters selection are given through simulations on traveling salesman problems (TSP). The results show that the hybrid PS-ACO algorithm has better convergence performance than genetic algorithm (GA), ACO and MMAS under the condition of limited evolution iterations.

Keywords

Ant colony optimizationParticle swarm optimizationHybrid PS-ACOTSP

Copyright information

© Springer Science+Business Media, LLC 2009