The canonical brain storm optimization (BSO) employs clustering, creating and selecting operators, which are all connected and have great impacts on the optimization performance. In this paper, the state-of-the-art search strategies are introduced and the potential strengths and weaknesses of these strategies in the BSO algorithm are analyzed and compared from the perspective of the analytical model rather than any metaphors. The numerical experiments are carried out to artificially amplify and highlight the performance of various strategies. Finally, the progressive directions of the BSO algorithm are discussed for further research.

中文翻译：

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头脑风暴优化中的搜索策略研究

规范的头脑风暴优化（BSO）采用聚类，创建和选择运算符，这些运算符都是相互联系的，对优化性能有很大影响。本文介绍了最新的搜索策略，并从分析模型而不是任何隐喻的角度分析和比较了这些策略在BSO算法中的潜在优势和劣势。进行数值实验是为了人为地放大和突出各种策略的性能。最后，讨论了BSO算法的发展方向，以作进一步研究。