Open AccessMultimodal optimization using whale optimization algorithm enhanced with local search and niching technique
Author(s) -
Hui Li,
Peng Zou,
Zhiguo Huang,
Chen Zeng,
Xiao Liu
Publication year - 2020
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2020001
Subject(s) - benchmark (surveying) , computer science , mathematical optimization , optimization algorithm , global optimization , optimization problem , local optimum , continuous optimization , algorithm , multi swarm optimization , artificial intelligence , mathematics , geodesy , geography
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.