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.

中文翻译：

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CBSO：具有混沌局部搜索的模因头脑风暴优化

头脑风暴优化（BSO）是一种新提出的优化算法，灵感来自人类头脑风暴过程。在其出现之后，已经引起了很多关注，并且已经进行了许多尝试来改善其性能。BSO的搜索能力已得到增强，但在开发阶段仍会陷入停滞状态。本文提出了一种新的方法，该方法将BSO与混沌局部搜索（CLS）结合在一起，以缓解这种情况。混沌具有随机性和遍历性。这些属性确保CLS可以在搜索持续时间足够长的情况下探索搜索空间的每种状态。引入CLS可以使BSO摆脱停滞，并同时保持人口的多样性，从而在勘探与开发之间实现更好的平衡。随机选择十二张混沌图以增加搜索机制的多样性。基于25个基准函数的实验和统计结果证明了该方法的优越性。