Skip to main content

Advertisement

Log in

Study on hybrid PS-ACO algorithm

  • Published:
Applied Intelligence Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Maniezzo V, Carbonaro A (2001) Ant colony optimization: an overview, essays and surveys in metaheuristics. Kluwer, Dordrecht, 21–44

    Google Scholar 

  2. Dorigo M, Gianni DC, Gambardella LM (1999) Ant algorithms for discrete optimization. Artif Life 5:137–172

    Article  Google Scholar 

  3. Stutzle T, Hoos HH (2000) MAX-MIN ant system. Future Gener Comput Syst 16(8):889–914

    Article  Google Scholar 

  4. Dorigo M, Maniezzo V, Colorni A (1996) The ant system: Optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern B 26(2):29–41

    Article  Google Scholar 

  5. Maniezzo V, Colorni A (1999) The Ant System applied to the quadratic assignment problem. IEEE Trans Data Knowl Eng 11(5):769–778

    Article  Google Scholar 

  6. Bullnheimer B, Hartl RF, Strauss C (1999) An improved ant system algorithm for the vehicle routing problem. Ann Oper Res 89:319–328

    Article  MATH  MathSciNet  Google Scholar 

  7. Gambardella LM, Dorigo M (2000) Ant Colony System hybridized with a new local search for the sequential ordering problem. INFORMS J Comput 12(3):237–255

    Article  MATH  MathSciNet  Google Scholar 

  8. Zwann S, Marques C (1999) Ant colony optimization for Job Shop scheduling. In: Proceedings of the third workshop on genetic algorithms and artificial life (GAAL 99)

  9. Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53–66

    Article  Google Scholar 

  10. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE conference neural networks, vol. IV, Piscataway, NJ, pp 1942–1948

  11. Shi YH, Eberhart RC (1998) A modified particle swarm optimizer. In: Proceedings of 1998 IEEE international conference on evolutionary computation, Anchorage, AK, pp 69-73

  12. Russell C, Eberhart, Shi YH (1998) In: Comparison between genetic algorithms and particle swarm optimization. Lecture notes in computer science, vol 1447. Springer, Berlin, pp 611–616

    Google Scholar 

  13. Parsopoulos KE, Vrahatis MN (2002) Recent approaches to global optimization problems through particle swarm optimization. Nat Comput 1:235–306

    Article  MATH  MathSciNet  Google Scholar 

  14. Larrañaga P, Kuijpers CMH, Murga RH (1999) Genetic algorithms for the travelling salesman problem: a review of representations and operators. Artif Intel Rev 13(2):129–170

    Article  Google Scholar 

  15. http://www.iwr.uni-heidelberg.de/groups/comopt/software/TSPLIB95/tsp/

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bing Shuang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Shuang, B., Chen, J. & Li, Z. Study on hybrid PS-ACO algorithm. Appl Intell 34, 64–73 (2011). https://doi.org/10.1007/s10489-009-0179-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-009-0179-6

Keywords

Navigation