A modified whale optimization algorithm with multi-strategy mechanism for global optimization problems

Whale Optimization Algorithm (WOA) is an outstanding nature-inspired algorithm widely used to solve many complex engineering optimization problems. However, WOA has a poor balance in exploration and exploitation, which converges to local optimum easily. This article proposes a Modified Whale Optimiz...

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Vydané v:	Neural computing & applications Ročník 37; číslo 27; s. 22339 - 22352
Hlavní autori:	Li, Mingyuan, Yu, Xiaobing, Fu, Bingbing, Wang, Xuming
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Springer London 01.09.2025 Springer Nature B.V
Predmet:	Algorithms Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Exploitation Foraging behavior Global optimization Heuristic Image Processing and Computer Vision Mutation Optimization algorithms Probability and Statistics in Computer Science S.I.: Hybrid Approaches to Nature-inspired Optimization Algorithms and Their Applications Special Issue on Hybrid Approaches to Nature-inspired Optimization Algorithms and Their Applications Whales & whaling Elite reverse learning strategy Whale optimization algorithm Nonlinear convergence factor Multi-strategy
ISSN:	0941-0643, 1433-3058
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Shrnutí:	Whale Optimization Algorithm (WOA) is an outstanding nature-inspired algorithm widely used to solve many complex engineering optimization problems. However, WOA has a poor balance in exploration and exploitation, which converges to local optimum easily. This article proposes a Modified Whale Optimization Algorithm (MWOA) with multi-strategy mechanism, which introduces the elite reverse learning strategy, nonlinear convergence factor, DE/rand/1 mutation strategy and Lévy flight disturbance strategy. MWOA can improve the convergent ability and maintain the balance of exploitation and exploration to avoid local optimum. Compared with WOA, PSO, MFO, SOA, SCA and other four WOA variants on the CEC2017 benchmark suite, MWOA has strong competitiveness and can better improve the efficiency of WOA according to the experimental results and analysis.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	0941-0643 1433-3058
DOI:	10.1007/s00521-023-08287-5