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Brain storm optimization using a slight relaxation selection and multi-population based creating ideas ensemble

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Abstract

Brain storm optimization is a swarm intelligence algorithm inspired by the brainstorming process in human beings. Many researchers have paid much more attention to it, and many attempts have been made to improve it’s performance. The search ability of brain storm optimization is maintained by the creating process of ideas, but it still suffers from sticking into stagnation during exploitation phase. This paper proposes a novel brain storm optimization variant, named RMBSO, in which a slight relaxation selection and multi-population based creating ideas ensemble are employed to improve the performance of brain storm optimization on global optimization problem with diverse landscapes. Firstly, the basic framework of original brain storm optimization is imbedded into multi-population based ensemble of heterogeneous but complementary creating ideas to make the algorithm jump out of stagnation with strong searching ability. Secondly, a new triangular mutation ruler and a simple partition of subpopulations are designed to better balance exploration and exploitation. Thirdly, a slight relaxation selection mechanism instead of greedy choice is first developed to keep the population’s diversity. Finally, extensive experiments on the suit of CEC 2015 benchmark functions and statistical comparisons are executed. Experimental results indicate that the proposed algorithm is significantly better than, or at least comparable to the state-of-the-art brain storm optimization variants and several improved differential evolution algorithms.

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Acknowledgments

This research is partly supported by the Natural Science Foundation of China (Grant No. 11371197 and 61971234), Humanity and Social Science Youth foundation of Ministry of Education of China (Grant No. 12YJCZH179), the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. 16KJA110001). Thanks all authors for providing the source codes of all comparison algorithms.

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Authors and Affiliations

  1. School of Mathematical Sciences, Nanjing Normal University, Nanjing, 210023, People’s Republic of China

    Yuehong Sun, Kelian Xiao, Jianyang Bao & Ye Jin

  2. Jiangsu Key Laboratory for Numerical Simulation of Large Scale Complex Systems, Nanjing, 210023, People’s Republic of China

    Yuehong Sun

  3. School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, 210003, People’s Republic of China

    Jianxiang Wei

  4. School of Science, Nanjing University of Posts and Telecommunications, Nanjing, 210023, People’s Republic of China

    Tingting Wu

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  1. Yuehong Sun
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Correspondence to Yuehong Sun.

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Sun, Y., Wei, J., Wu, T. et al. Brain storm optimization using a slight relaxation selection and multi-population based creating ideas ensemble. Appl Intell 50, 3137–3161 (2020). https://doi.org/10.1007/s10489-020-01690-8

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  • DOI: https://doi.org/10.1007/s10489-020-01690-8

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