Brain Storm Optimization Algorithm based on Competition Mechanism

Li, Fenqiang; Peng, Hu; Deng, Changshou; Zeng, Yi; Tan, Yucheng; Wang, Wenjun

doi:10.1109/cec.2019.8790330

Cited by 1 publication

(2 citation statements)

References 21 publications

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“…There is no need to calculate the distance between individuals in BSO-OS, which greatly reduces the computational burden. Similarly, in [19], Li et al proposed the BSO based on a competition mechanism, which designed two competing groups in the same way.…”

Section: Objective Space-based Clustering Strategiesmentioning

confidence: 99%

“…On the one hand, different grouping algorithms were proposed to replace k-means, such as simple grouping method (SGM) [40], random grouping strategy (RGS) [4]. Besides, a class of objective space-based grouping algorithms [11,19,30] could greatly reduce the time cost, which was first used in BSO-OS (BSO in objective space) proposed by Shi [30]. On the other hand, some work tried to reduce the times of calling for k-means [3,5] or controlled the number of iterations of k-means [43].…”

Section: Introductionmentioning

confidence: 99%

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BSO20: efficient brain storm optimization for real-parameter numerical optimization

Luo

Lin

et al. 2021

Complex Intell. Syst.

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Brain storm optimization (BSO) is an emerging global optimization algorithm. The primary idea is to divide the population into different clusters, and offspring are generated within a cluster or between two clusters. However, the problems of inefficient clustering strategy and insufficient exploration exist in BSO. In this paper, a novel and efficient BSO is proposed, called BSO20 (proposed in 2020). BSO20 pays attention to both the clustering strategy and the mutation strategy. First, we propose a hybrid clustering strategy, which combines two clustering strategies, i.e., nearest-better clustering and random grouping strategy. The size of the subpopulation clustered by two strategies is dynamically adjusted as the population evolves. Second, a modified mutation strategy is used in BSO20 to share information within a cluster or among multiple clusters to enhance the ability of exploration. BSO20 is tested on the problems of the 2017 IEEE Congress on Evolutionary Computation competition on real parameter numerical optimization. BSO20 is compared with several variants of BSO and two variants of particle swarm optimization, and the experimental results show that BSO20 is competitive.

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Section: Objective Space-based Clustering Strategiesmentioning

confidence: 99%