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. Keywords Brain storm optimization · Clustering · Creating · Selecting 1 Introduction does not require that the optimization problem be differen- tiable as is required by classic optimization methods, which Swarm intelligence (SI) is the study of computational sys- has made the BSO algorithm to be a highly effective way to tems, which mimic the emergent collective behavior exhib- the problems in the real-world community. ited by natural or artiﬁcial entities. Examples of biological The canonical BSO employs three strategies: clustering SI are found in ant colonies, bird ﬂocking, ﬁsh school- operator, creating operator and selecting operator. Specif- ing, chiropteran ﬂying, quadruped animal herding, bacteria ically, clustering operator organizes
Memetic Computing – Springer Journals
Published: Jun 1, 2018
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