An Improved Brain Storm Optimization with Learning Strategy

Wang, Hong; Liu, Jia; Yi, Wenjie; Niu, Ben; Baek, Jongduk

doi:10.1007/978-3-319-61824-1_56

Cited by 8 publications

(5 citation statements)

References 15 publications

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“…This paper aims to propose a novel learning mechanism to generate the new candidate solutions for BSO algorithms. Therefore, we choose the most representative SI algorithm PSO [ 14 ], the original BSO algorithm [ 15 ] and the other BSO variant BSOLS with learning strategy [ 16 ]. The hyperparameters setting for each algorithm is given in Table 2 .…”

Section: Methodsmentioning

confidence: 99%

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BSO-CLS: Brain Storm Optimization Algorithm with Cooperative Learning Strategy

Duan

Yang

et al. 2020

Lecture Notes in Computer Science

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Brain storm optimization algorithms (BSO) have shown great potential in many global black-box optimization problems. However, the existing BSO variants can suffer from three problems: (1) large-scale optimization problem; (2) hyperparameter optimization problem; (3) high computational cost of the clustering operations. To address these problems, in this paper, we propose a simple yet effective BSO variant named Brain Storm Optimization Algorithm with Cooperative Learning Strategy (BSO-CLS). It is inspired by the new ideas generating process of brain storm in which the participators propose their own ideas by cooperatively learning other participators’ ideas. Thus, BSO-CLS iteratively updates the candidate solutions by linearly combining other solutions with the weights deriving from the fitness values of other solutions. To validate the effectiveness of the proposed method, we test it on 6 benchmark functions with the 1000 dimensions. The experimental results show that BSO-CLS can outperform the vanilla BSO and the other BSO variant with the learning strategy.

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Section: Methodsmentioning

confidence: 99%

“…Recently, some learning based new solutions generating strategies have been proposed. BSOLS [ 16 ] proposed a novel learning strategy after updating operator to improve the diversity of population. OLBSO [ 9 ] introduced a orthogonal learning (OL) framework and incorporated it with BSO to improve the performance.…”

Section: Related Workmentioning

confidence: 99%

BSO-CLS: Brain Storm Optimization Algorithm with Cooperative Learning Strategy

Duan

Yang

et al. 2020

Lecture Notes in Computer Science

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“…Yang et al 25 designed an advanced discussion mechanism (ADMBSO) with inter and intra communication to achieve large‐scale and small‐scale searches. Wang et al 31 utilized an advanced learning method in which the poor individuals improve their own quality by gaining knowledge from the superior individuals. Li et al 32 developed a new generating strategy with three multi‐information interactions to enhance interaction capability.…”

Section: Original Bso and Related Workmentioning

confidence: 99%

An improved brain storm optimization with chunking‐grouping method

Guo,

Gao,

Jiang

et al. 2023

Concurrency and Computation

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SummaryBrain storm optimization (BSO) is a population‐based intelligence algorithm for optimization problems, which has attracted researchers' growing attention due to its simplicity and efficiency. An improved BSO, called CIBSO, is presented in this article. First of all, a new grouping method, in which the population is partitioned into chunks according to the fitness and recombined to groups, is developed to balance each group with same quality‐level. Afterwards, a new mutation strategy is designed in CIBSO and a learning mechanism is used to adaptively select appropriate strategy. Experiments on the CEC2014 test suite indicate that CIBSO is better or at least competitive performance against the compared BSO variants.

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“…The first is based on the mutation strategy of the classic BSO, such as the improvements of the update strategy of step size [4,10,24,42], the mutation strategy adaptive selection [38]. The second contains several new mutation strategies, such as modified BSO (MBSO) [40], advanced discussion mechanism-based BSO (ADMBSO) [35], BSO with learning strategy (BSOLS) [32], adaptive BSO with multiple strategies (AMBSO) [9], active learning BSO (ALBSO) [5]. The third is hybrid algorithms formed by BSO and existing algorithms, such as BSO with a chaotic operation (BSO-CO) [36], BSO with differential evolution [2], hybrid BSO and simulate annealing [16], and hybrid covariance matrix adaptive evolution strategy and global-best BSO [13].…”

Section: Introductionmentioning

confidence: 99%

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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An Improved Brain Storm Optimization with Learning Strategy

Cited by 8 publications

References 15 publications

BSO-CLS: Brain Storm Optimization Algorithm with Cooperative Learning Strategy

BSO-CLS: Brain Storm Optimization Algorithm with Cooperative Learning Strategy

An improved brain storm optimization with chunking‐grouping method

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

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