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Whale optimization algorithm based on nonlinear convergence factor and chaotic inertial weight
Author(s) -
Ding Hangqi,
Wu Zhiyong,
Zhao Luchen
Publication year - 2020
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5949
Subject(s) - chaotic , convergence (economics) , benchmark (surveying) , initialization , mathematical optimization , computer science , nonlinear system , particle swarm optimization , algorithm , population , firefly algorithm , mathematics , artificial intelligence , physics , demography , geodesy , quantum mechanics , sociology , geography , economics , programming language , economic growth
Summary The whale optimization algorithm (WOA) is a new biological meta‐heuristic algorithm based on the social hunting behaviors of humpback whales. However, it can easily fall into a local optimum when solving complex problems and exhibits slow convergence speed and poor exploration. This study proposed three improved versions of the WOA based on the concepts of chaos initialization, nonlinear convergence factor, and chaotic inertial weight to enhance its exploration abilities. These properties were employed to improve the population diversity and maintain the balance between exploration and exploitation. The performance of the best version was compared with those of moth‐flame optimization, firefly algorithm, particle swarm optimization, gray wolf optimizer, flower pollination algorithm, original WOA, and two recently proposed hybrid WOA through 19 benchmark functions. Experimental results indicated that the proposed algorithms exhibit better performance in terms of complexity and convergence speed.