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A Multiobjective Brain Storm Optimization Algorithm Based on Decomposition

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  • Cai Dai
  • Xiujuan Lei

Abstract

Brain storm optimization (BSO) algorithm is a simple and effective evolutionary algorithm. Some multiobjective brain storm optimization algorithms have low search efficiency. This paper combines the decomposition technology and multiobjective brain storm optimization algorithm (MBSO/D) to improve the search efficiency. Given weight vectors transform a multiobjective optimization problem into a series of subproblems. The decomposition technology determines the neighboring clusters of each cluster. Solutions of adjacent clusters generate new solutions to update population. An adaptive selection strategy is used to balance exploration and exploitation. Besides, MBSO/D compares with three efficient state-of-the-art algorithms, e.g., NSGAII and MOEA/D, on twenty-two test problems. The experimental results show that MBSO/D is more efficient than compared algorithms and can improve the search efficiency for most test problems.

Suggested Citation

  • Cai Dai & Xiujuan Lei, 2019. "A Multiobjective Brain Storm Optimization Algorithm Based on Decomposition," Complexity, Hindawi, vol. 2019, pages 1-11, January.
    • Handle: RePEc:hin:complx:5301284
    DOI: 10.1155/2019/5301284
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    References listed on IDEAS

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    1. Yuhui Shi, 2011. "An Optimization Algorithm Based on Brainstorming Process," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 2(4), pages 35-62, October.
    2. 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.
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    Cited by:

    1. Alberto Pajares & Xavier Blasco & Juan Manuel Herrero & Miguel A. Martínez, 2021. "A Comparison of Archiving Strategies for Characterization of Nearly Optimal Solutions under Multi-Objective Optimization," Mathematics, MDPI, vol. 9(9), pages 1-28, April.

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