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A multiobjective immune algorithm based on a multiple-affinity model

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  • Hu, Zhi-Hua

Abstract

This paper presents a new multiobjective immune algorithm based on a multiple-affinity model inspired by immune system (MAM-MOIA). The multiple-affinity model builds the relationship model among main entities and concepts in multiobjective problems (MOPs) and multiobjective evolutionary algorithms (MOEAs), including feasible solution, variable space, objective space, Pareto-optimal set, ranking and crowding distance. In the model, immune operators including clonal proliferation, hypermutation and immune suppression are designed to proliferate superior antibodies and suppress the inferiors. MAM-MOIA is compared with NSGA-II, SPEA2 and NNIA in solving the ZDT and DTLZ standard test problems. The experimental study based on three performance metrics including coverage of two sets, convergence and spacing proves that MAM-MOIA is effective for solving MOPs.

Suggested Citation

  • Hu, Zhi-Hua, 2010. "A multiobjective immune algorithm based on a multiple-affinity model," European Journal of Operational Research, Elsevier, vol. 202(1), pages 60-72, April.
  • Handle: RePEc:eee:ejores:v:202:y:2010:i:1:p:60-72
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    References listed on IDEAS

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    1. Tan, K.C. & Goh, C.K. & Mamun, A.A. & Ei, E.Z., 2008. "An evolutionary artificial immune system for multi-objective optimization," European Journal of Operational Research, Elsevier, vol. 187(2), pages 371-392, June.
    2. Elaoud, Semya & Loukil, Taicir & Teghem, Jacques, 2007. "The Pareto fitness genetic algorithm: Test function study," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1703-1719, March.
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    Cited by:

    1. Lei, Yu & Gong, Maoguo & Zhang, Jun & Li, Wei & Jiao, Licheng, 2014. "Resource allocation model and double-sphere crowding distance for evolutionary multi-objective optimization," European Journal of Operational Research, Elsevier, vol. 234(1), pages 197-208.

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