An Automatic Niching Particle Swarm for Multimodal Function Optimization

  • Yu Liu
  • Zhaofa Yan
  • Wentao Li
  • Mingwei Lv
  • Yuan Yao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6145)


Niching is an important technique for mutlimodal optimization. This paper proposed an improved niching technique based on particle swarm optimizer to locate multiple optima. In the proposed algorithm, the algorithm inspired from natural ecosystem form niches automatically without any prespecified problem dependent parameters. Experiment results demonstrated that the proposed niching method is superior to the classic niching methods which are with or without niching parameters.


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  1. 1.
    Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, vol. 43. IEEE, New York (1995)Google Scholar
  2. 2.
    Kennedy, J., Eberhart, R., et al.: Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, vol. 4, pp. 1942–1948. IEEE, Piscataway (1995)CrossRefGoogle Scholar
  3. 3.
    Mahfoud, S.: Crowding and preselection revisited. Urbana (51) 61801Google Scholar
  4. 4.
    Goldberg, D., Richardson, J.: Genetic algorithms with sharing for multimodal function optimization. In: Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application table of contents, pp. 41–49. L. Erlbaum Associates Inc., Hillsdale (1987)Google Scholar
  5. 5.
    Yin, X., Germany, N.: A fast genetic algorithm with sharing scheme using cluster analysis methods in multimodal function optimization. In: Artificial neural nets and genetic algorithms: proceedings of the international conference, Innsbruck, Austria, vol. 6, p. 450. Springer, Heidelberg (1993)Google Scholar
  6. 6.
    Petrowski, A.: A clearing procedure as a niching method for genetic algorithms. In: Proceedings of the 1996 IEEE International Conference on Evolutionary Computation, Citeseer, pp. 798–803 (1996)Google Scholar
  7. 7.
    Li, J., Balazs, M., Parks, G., Clarkson, P.: A species conserving genetic algorithm for multimodal function optimization. Evolutionary computation 10, 207–234 (2002)CrossRefGoogle Scholar
  8. 8.
    Iwamatsu, M.: Locating all the global minima using multi-species particle swarm optimizer: The inertia weight and the constriction factor variants. In: IEEE Congress on Evolutionary Computation, CEC 2006, pp. 816–822 (2006)Google Scholar
  9. 9.
    Iwamatsu, M.: Multi-species particle swarm optimizer for multimodal function optimization. IEICE Transactions on Information and Systems 89, 1181–1187 (2006)CrossRefGoogle Scholar
  10. 10.
    Clerc, M., Kennedy, J.: The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation 6, 58–73 (2002)CrossRefGoogle Scholar
  11. 11.
    Kennedy, J., Mendes, R.: Population structure and particle swarm performance, pp. 1671–1676 (2002)Google Scholar
  12. 12.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: NSGA-II. IEEE Transactions on Evolutionary Computation 6 (2002)Google Scholar
  13. 13.
    Deb, K.: Genetic algorithms in multimodal function optimization. Master’s thesis, The University of Alabama (1989)Google Scholar
  14. 14.
    Li, X.: Adaptively choosing neighbourhood bests using species in a particle swarm optimizer for multimodal function optimization. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 105–116. Springer, Heidelberg (2004)Google Scholar
  15. 15.
    Li, X.: Niching without niching parameters: Particle swarm optimization using a ring topology. IEEE Transactions on Evolutionary Computation 14, 150–169 (2010)CrossRefGoogle Scholar
  16. 16.
    Ackley, D.: An empirical study of bit vector function optimization. Genetic algorithms and simulated annealing 1, 170–204 (1987)Google Scholar
  17. 17.
    Michalewicz, Z.: Genetic algorithms+ data structures. Springer, Heidelberg (1996)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Yu Liu
    • 1
    • 2
  • Zhaofa Yan
    • 1
    • 2
  • Wentao Li
    • 1
    • 2
  • Mingwei Lv
    • 1
    • 2
  • Yuan Yao
    • 3
  1. 1.School of SoftwareDalian University of TechnologyDalianP.R. China
  2. 2.Institute of IT Service Engineering and ManagementDalianP.R. China
  3. 3.Shanghai Key Laboratory of Machine Automation and Robotics 

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