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A hybrid immune multiobjective optimization algorithm

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  • Chen, Jianyong
  • Lin, Qiuzhen
  • Ji, Zhen

Abstract

In this paper, we develop a hybrid immune multiobjective optimization algorithm (HIMO) based on clonal selection principle. In HIMO, a hybrid mutation operator is proposed with the combination of Gaussian and polynomial mutations (GP-HM operator). The GP-HM operator adopts an adaptive switching parameter to control the mutation process, which uses relative large steps in high probability for boundary individuals and less-crowded individuals. With the generation running, the probability to perform relative large steps is reduced gradually. By this means, the exploratory capabilities are enhanced by keeping a desirable balance between global search and local search, so as to accelerate the convergence speed to the true Pareto-optimal front in the global space with many local Pareto-optimal fronts. When comparing HIMO with various state-of-the-art multiobjective optimization algorithms developed recently, simulation results show that HIMO performs better evidently.

Suggested Citation

  • Chen, Jianyong & Lin, Qiuzhen & Ji, Zhen, 2010. "A hybrid immune multiobjective optimization algorithm," European Journal of Operational Research, Elsevier, vol. 204(2), pages 294-302, July.
    • Handle: RePEc:eee:ejores:v:204:y:2010:i:2:p:294-302
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    References listed on IDEAS

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    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.
    2. Lin, Qiuzhen & Li, Jianqiang & Du, Zhihua & Chen, Jianyong & Ming, Zhong, 2015. "A novel multi-objective particle swarm optimization with multiple search strategies," European Journal of Operational Research, Elsevier, vol. 247(3), pages 732-744.

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