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. 2024 Mar 18;14(1):6432.
doi: 10.1038/s41598-024-56919-0.

Global-best brain storm optimization algorithm based on chaotic difference step and opposition-based learning

Yanchi Zhao  1 , Jianhua Cheng  2 , Jing Cai  3 , Bing Qi  1
Affiliations

Global-best brain storm optimization algorithm based on chaotic difference step and opposition-based learning

Yanchi Zhao et al. Sci Rep. .

Abstract

Recently, the following global-best strategy and discussion mechanism have been prevailing to solve the slow convergence and the low optimization accuracy in the brain storm optimization (BSO) algorithm. However, the traditional BSO algorithm also suffers from the problem that it is easy to fall into local optimum. Therefore, this work innovatively designed the chaotic difference step strategy. This strategy introduced four commonly used chaotic maps and difference step to expand the population search space to improve the situation. Moreover, opposition-based learning thought was innovatively adopted into the BSO algorithm. The thought aims to generate the opposition-based population, increase the search density, and make the algorithm out of the local optimum as soon as possible. In summary, this work proposed a global-best brain storm optimization algorithm based on the chaotic difference step and opposition-based learning (COGBSO). According to the CEC2013 benchmark test suit, 15 typical benchmark functions were selected, and multiple sets of simulation experiments were conducted on MATLAB. The COGBSO algorithm was also compared to recent competitive algorithms based on the complete CEC2018 benchmark test suit. The results demonstrate that the COGBSO outperforms BSO and other improved algorithms in solving complex optimization problems.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
The number of papers on brain storm optimization since 2011.
Algorithm 1
Algorithm 1
The BSO algorithm.
Figure 2
Figure 2
Schematic diagram of the chaotic difference step.
Figure 3
Figure 3
The distribution of the four chaotic maps.
Figure 4
Figure 4
The trigger condition and end condition.
Algorithm 2
Algorithm 2
The COGBSO algorithm.
Figure 5
Figure 5
The convergence curves of four typical benchmark functions on 10-D problems.
Figure 6
Figure 6
The convergence curves of four typical benchmark functions on 20-D problems.
Figure 7
Figure 7
The convergence curves of four typical benchmark functions on 30-D problems.
Figure 8
Figure 8
Boxplots between algorithms on F1, F3, F10, F11.
Figure 9
Figure 9
The convergence curves of F10 function based on the idea of ablation experiments.
See this image and copyright information in PMC

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