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Multi-Objective Optimization Based on Brain Storm Optimization Algorithm

Author

Listed:
  • Yuhui Shi

    (Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China)

  • Jingqian Xue

    (Huawei, Xi’an, China)

  • Yali Wu

    (Xi’an University of Technology, Xi’an, China)

Abstract

In recent years, many evolutionary algorithms and population-based algorithms have been developed for solving multi-objective optimization problems. In this paper, the authors propose a new multi-objective brain storm optimization algorithm in which the clustering strategy is applied in the objective space instead of in the solution space in the original brain storm optimization algorithm for solving single objective optimization problems. Two versions of multi-objective brain storm optimization algorithm with different characteristics of diverging operation were tested to validate the usefulness and effectiveness of the proposed algorithm. Experimental results show that the proposed multi-objective brain storm optimization algorithm is a very promising algorithm, at least for solving these tested multi-objective optimization problems.

Suggested Citation

  • Yuhui Shi & Jingqian Xue & Yali Wu, 2013. "Multi-Objective Optimization Based on Brain Storm Optimization Algorithm," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 4(3), pages 1-21, July.
  • Handle: RePEc:igg:jsir00:v:4:y:2013:i:3:p:1-21
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    Cited by:

    1. Cai Dai & Xiujuan Lei, 2019. "A Multiobjective Brain Storm Optimization Algorithm Based on Decomposition," Complexity, Hindawi, vol. 2019, pages 1-11, January.

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