Abstract
Multimodal optimization is one of the most challenging tasks for optimization. The difference between multimodal optimization and single objective optimization problem is that the former needs to find both multiple global and local optima at the same time. A novel swarm intelligent method, Self-adaptive Brain Storm Optimization (SBSO) algorithm, is proposed to solve multimodal optimization problems in this paper. In order to obtain potential multiple global and local optima, a max-fitness grouping cluster method is used to divide the ideas into different sub-groups. And different sub-groups can help to find the different optima during the search process. Moreover, the self-adaptive parameter control is applied to adjust the exploration and exploitation of the proposed algorithm. Several multimodal benchmark functions are used to evaluate the effectiveness and efficiency. Compared with the other competing algorithms reported in the literature, the new algorithm can provide better solutions and show good performance.
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Guo, X., Wu, Y., Xie, L. (2014). Modified Brain Storm Optimization Algorithm for Multimodal Optimization. In: Tan, Y., Shi, Y., Coello, C.A.C. (eds) Advances in Swarm Intelligence. ICSI 2014. Lecture Notes in Computer Science, vol 8795. Springer, Cham. https://doi.org/10.1007/978-3-319-11897-0_40
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DOI: https://doi.org/10.1007/978-3-319-11897-0_40
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