Enhancing the Local Search Ability of the Brain Storm Optimization Algorithm by Covariance Matrix Adaptation

Seham Elsayed; Mohammed El-abd; Karam Sallam

doi:10.1007/978-3-030-15070-9_5

Enhancing the Local Search Ability of the Brain Storm Optimization Algorithm by Covariance Matrix Adaptation

Seham Elsayed, Mohammed El-abd, Karam Sallam

Research output: A Conference proceeding or a Chapter in Book › Chapter › peer-review

1 Citation (Scopus)

Abstract

Recently, the Brain Storm Optimization (BSO) algorithm has attracted many researchers and practitioners attention from the evolutionary computation community. However, like many other population based algorithms, BSO shows good performance at global exploration but not good enough at local exploitation. To alleviate this issue, in this chapter, the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is utilized in the Global-best BSO (GBSO), with the aim to combine the exploration ability of BSO and local ability of CMA-ES and to design an improved version of BSO. The performance of the proposed algorithm is tested by solving 28 classical optimization problems and the proposed algorithm is shown to perform better than GBSO.

Original language	English
Title of host publication	Brain Storm Optimization Algorithms
Editors	Shi Cheng, Yuhui Shi
Place of Publication	Netherlands
Publisher	Springer
Chapter	5
Pages	105-122
Number of pages	18
ISBN (Electronic)	9783030150709
ISBN (Print)	9783030150693
DOIs	https://doi.org/10.1007/978-3-030-15070-9_5
Publication status	Published - 4 Jun 2019
Externally published	Yes

Publication series

Name	Adaptation, Learning, and Optimization
Volume	23
ISSN (Print)	1867-4534
ISSN (Electronic)	1867-4542

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10.1007/978-3-030-15070-9_5

Cite this

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abstract = "Recently, the Brain Storm Optimization (BSO) algorithm has attracted many researchers and practitioners attention from the evolutionary computation community. However, like many other population based algorithms, BSO shows good performance at global exploration but not good enough at local exploitation. To alleviate this issue, in this chapter, the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is utilized in the Global-best BSO (GBSO), with the aim to combine the exploration ability of BSO and local ability of CMA-ES and to design an improved version of BSO. The performance of the proposed algorithm is tested by solving 28 classical optimization problems and the proposed algorithm is shown to perform better than GBSO.",

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Enhancing the Local Search Ability of the Brain Storm Optimization Algorithm by Covariance Matrix Adaptation. / Elsayed, Seham; El-abd, Mohammed; Sallam, Karam.
Brain Storm Optimization Algorithms. ed. / Shi Cheng; Yuhui Shi. Netherlands: Springer, 2019. p. 105-122 (Adaptation, Learning, and Optimization; Vol. 23).

Research output: A Conference proceeding or a Chapter in Book › Chapter › peer-review

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T1 - Enhancing the Local Search Ability of the Brain Storm Optimization Algorithm by Covariance Matrix Adaptation

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Enhancing the Local Search Ability of the Brain Storm Optimization Algorithm by Covariance Matrix Adaptation

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