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Multimodal optimization using whale optimization algorithm enhanced with local search and niching technique

Hui Li Peng Zou Zhiguo Huang Chenbo Zeng Xiao Liu

*Corresponding author: Hui Li ray@mail.buct.edu.cn

MBE2020,1,1doi:10.3934/mbe.2020001

For some real-world problems, it is desirable to find multiple global optima as many as possible. The multimodal optimization approach which finds multiple optima in a single run shows significant difference with the single modal optimization approach.The whale optimization algorithm (WOA) is a newly emerging reputable optimization algorithm. Its global search ability has been verified in many benchmark functions and real-world applications. In this paper, we propose a multimodal version of whale optimization algorithm (MMWOA). MMWOA enhances the multimodal search ability of WOA by using the niching technique and improves the local search efficiency of WOA by combining the Gaussian sampling technique. The algorithm has been tested on multimodal optimization benchmark functions recommended by CEC’2013 and on a multimodal optimization problem with non-linear constraints. Experimental results indicate that MMWOA has competitive performance compared with other state-of-the-art multimodal optimization algorithms.

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