Handling Multi-optimization with Gender-Hierarchy Based Particle Swarm Optimizer

  • Wei Wei
  • Weihui Zhang
  • Yuan Jiang
  • Hao Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7331)


In this study, we present a novel particle swarm optimizer, called Gender-Hierarchy Based Particle Swarm Optimizer (GH-PSO), to handle multi-objective optimization problems. By employing the concepts of gender and hierarchy to particles, both the exploration ability and the exploitation skill are extended. In order to maintain an uniform distribution of non-dominated solutions, a novel proposal, called Rectilinear Distance based Selection and Replacement (RDSR), is also proposed. The proposed algorithm is validated by using several benchmark functions and metrics. The results show that the proposed algorithm outperforms over MOPSO, NSGA-II and PAES-II.


Gender Hierarchy Particle Swarm Optimizer Multi-objective Optimization Rectilinear Distance 


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  1. 1.
    Moore, J., Chapman, R., Dozier, G.: Multiobjective Particle Swarm Optimization. In: ACM-SE 38: Proceedings of the 38th Annual on Southeast Regional Conference, pp. 56–57 (2000)Google Scholar
  2. 2.
    Coello Coello, C.A., Pulido, G.T., Lechuga, M.S.: Handling Multiple Objectives With Particle Swarm Optimization. IEEE Transactions on Evolutionary Computation 8(3), 256–279 (2004)CrossRefGoogle Scholar
  3. 3.
    Moore, J., Chapman, R.: Application of Particle Swarm to Multiobjective Optimization. Dept. of Computer Science Software Engineering, Auburn University (1999)Google Scholar
  4. 4.
    Ray, T., Liew, K.M.: A swarm metaphor for multiobjective design optimization. Engineering Optimization 34(2), 141–153 (2002)CrossRefGoogle Scholar
  5. 5.
    Hu, X., Eberhart, R.: Multiobjective optimization using dynamic neighborhood particle swarm optimization. In: Proceedings of the 2002 Congress on Evolutionary Computation (CEC 2002), vol. 2, pp. 1677–1681 (2002)Google Scholar
  6. 6.
    Li, X.: A Non-dominated Sorting Particle Swarm Optimizer for Multiobjective Optimization. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 37–48. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  7. 7.
    Parsopoulos, K.E., Vrahatis, M.N.: Particle swarm optimization method in multiobjective problems. In: Proceedings of the 2002 ACM Symposium on Applied Computing, pp. 603–607 (2002)Google Scholar
  8. 8.
    Gao, J., Li, H., Hu, L.: Gender-Hierarchy Particle Swarm Optimizer Based on Punishment. In: Tan, Y., Shi, Y., Tan, K.C. (eds.) ICSI 2010. LNCS, vol. 6145, pp. 94–101. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Schaffer, J.D.: Multiple Objective Optimization with Vector Evaluated Genetic Algorithms. Ph.D. thesis, Vanderbilt University, Nashville, Tennessee (1984)Google Scholar
  10. 10.
    Kursawe, F.: A Variant of Evolution Strategies for Vector Optimization. In: Schwefel, H.-P., Männer, R. (eds.) PPSN 1990. LNCS, vol. 496, pp. 193–197. Springer, Heidelberg (1991)CrossRefGoogle Scholar
  11. 11.
    Knowles, J.D., Corne, D.W.: Approximating the Non-dominated Front using the Pareto Archived Evolution Strategy. Evolutionary Computation 8(2), 149–172 (2000)CrossRefGoogle Scholar
  12. 12.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 182–197 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Wei Wei
    • 1
  • Weihui Zhang
    • 2
  • Yuan Jiang
    • 1
  • Hao Li
    • 2
  1. 1.China Jiliang UniversityHangzhouChina
  2. 2.Department of Computer ScienceZhejiang University of TechnologyHangzhouChina

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