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

Hui Li; Peng Zou; Zhiguo Huang; Chenbo Zeng; Xiao Liu; Hui Li; Peng Zou; Zhiguo Huang; Chenbo Zeng; Xiao Liu

doi:10.3934/mbe.2020001

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 1: 1-27. doi: 10.3934/mbe.2020001

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Research article Special Issues

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

Department of Computer Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China

Received: 01 June 2019 Accepted: 10 September 2019 Published: 23 September 2019

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.
- whale optimization algorithm,
- local search,
- clustering,
- niching algorithms
Citation: Hui Li, Peng Zou, Zhiguo Huang, Chenbo Zeng, Xiao Liu. Multimodal optimization using whale optimization algorithm enhanced with local search and niching technique[J]. Mathematical Biosciences and Engineering, 2020, 17(1): 1-27. doi: 10.3934/mbe.2020001

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Abstract

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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