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Published in: Memetic Computing 4/2018

27-12-2017 | Regular Research Paper

CBSO: a memetic brain storm optimization with chaotic local search

Authors: Yang Yu, Shangce Gao, Shi Cheng, Yirui Wang, Shuangyu Song, Fenggang Yuan

Published in: Memetic Computing | Issue 4/2018

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Abstract

Brain storm optimization (BSO) is a newly proposed optimization algorithm inspired by human being brainstorming process. After its appearance, much attention has been paid on and many attempts to improve its performance have been made. The search ability of BSO has been enhanced, but it still suffers from sticking into stagnation during exploitation phase. This paper proposes a novel method which incorporates BSO with chaotic local search (CLS) with the purpose of alleviating this situation. Chaos has properties of randomicity and ergodicity. These properties ensure CLS can explore every state of the search space if the search time duration is long enough. The incorporation of CLS can make BSO break the stagnation and keep the population’s diversity simultaneously, thus realizing a better balance between exploration and exploitation. Twelve chaotic maps are randomly selected for increasing the diversity of the search mechanism. Experimental and statistical results based on 25 benchmark functions demonstrate the superiority of the proposed method.

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Metadata
Title
CBSO: a memetic brain storm optimization with chaotic local search
Authors
Yang Yu
Shangce Gao
Shi Cheng
Yirui Wang
Shuangyu Song
Fenggang Yuan
Publication date
27-12-2017
Publisher
Springer Berlin Heidelberg
Published in
Memetic Computing / Issue 4/2018
Print ISSN: 1865-9284
Electronic ISSN: 1865-9292
DOI
https://doi.org/10.1007/s12293-017-0247-0

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