Whale optimization algorithm based on nonlinear convergence factor and chaotic inertial weight. (10th August 2020)

• Record Type: Journal Article
• Authors: Ding, Hangqi; Wu, Zhiyong; Zhao, Luchen
• Is Part Of: Concurrency and computation. Volume 32:Number 24(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-08-10
• Subjects: Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5949
• Languages: English
• ISSNs: 1532-0626

Abstract

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.