Brain storm optimization using a slight relaxation selection and multi-population based creating ideas ensemble
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Brain storm optimization using a slight relaxation selection and multi-population based creating ideas ensemble Yuehong Sun1,2 · Jianxiang Wei3 · Tingting Wu4 · Kelian Xiao1 · Jianyang Bao1 · Ye Jin1
© Springer Science+Business Media, LLC, part of Springer Nature 2020
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. Keywords Brain storm optimization · Multi-population · Creating ideas · Slight relaxation selection mechanism
1 Introduction Complex real world optimization problems need to be tackled with effective algorithms in different fields such as engineering, social sciences, astronautics, and so on. These problems involve global optimization over continuous
Yuehong Sun
[email protected] 1
School of Mathematical Sciences, Nanjing Normal University, Nanjing 210023, People’s Republic of China
2
Jiangsu Key Laboratory for Numerical Simulation of Large Scale Complex Systems, Nanjing 210023, People’s Republic of China
3
School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, 210003, People’s Republic of China
4
School of Science, Nanjing University of Posts and Telecommunications, Nanjing, 210023, People’s Republic of China
spaces. Hence, global optimization problems have become more and more complex, from simple unimodal functions to hybrid rotated shifted multimodal functions [1]. These led researchers to develop many of optimization algorithms. In recent years, the optimization techniques based on the imitation of biological mechanisms or natural phenomena has attracted a lot of attent
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