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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This research was partially supported by the JSPS KAKENHI Grant Number JP17K12751.
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Yu, Y., Gao, S., Cheng, S. et al. CBSO: a memetic brain storm optimization with chaotic local search. Memetic Comp. 10, 353–367 (2018). https://doi.org/10.1007/s12293-017-0247-0
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DOI: https://doi.org/10.1007/s12293-017-0247-0