Applied Intelligence

, Volume 34, Issue 1, pp 64–73

Study on hybrid PS-ACO algorithm



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.


Ant colony optimization Particle swarm optimization Hybrid PS-ACO TSP 


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  1. 1.
    Maniezzo V, Carbonaro A (2001) Ant colony optimization: an overview, essays and surveys in metaheuristics. Kluwer, Dordrecht, 21–44 Google Scholar
  2. 2.
    Dorigo M, Gianni DC, Gambardella LM (1999) Ant algorithms for discrete optimization. Artif Life 5:137–172 CrossRefGoogle Scholar
  3. 3.
    Stutzle T, Hoos HH (2000) MAX-MIN ant system. Future Gener Comput Syst 16(8):889–914 CrossRefGoogle Scholar
  4. 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 CrossRefGoogle Scholar
  5. 5.
    Maniezzo V, Colorni A (1999) The Ant System applied to the quadratic assignment problem. IEEE Trans Data Knowl Eng 11(5):769–778 CrossRefGoogle Scholar
  6. 6.
    Bullnheimer B, Hartl RF, Strauss C (1999) An improved ant system algorithm for the vehicle routing problem. Ann Oper Res 89:319–328 MATHCrossRefMathSciNetGoogle Scholar
  7. 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 MATHCrossRefMathSciNetGoogle Scholar
  8. 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) Google Scholar
  9. 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 CrossRefGoogle Scholar
  10. 10.
    Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE conference neural networks, vol. IV, Piscataway, NJ, pp 1942–1948 Google Scholar
  11. 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 Google Scholar
  12. 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. 13.
    Parsopoulos KE, Vrahatis MN (2002) Recent approaches to global optimization problems through particle swarm optimization. Nat Comput 1:235–306 MATHCrossRefMathSciNetGoogle Scholar
  14. 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 CrossRefGoogle Scholar
  15. 15.

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.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 UniversityShanghaiChina

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