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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Vydáno v:Neural computing & applications Ročník 37; číslo 27; s. 22339 - 22352
Hlavní autoři: Li, Mingyuan, Yu, Xiaobing, Fu, Bingbing, Wang, Xuming
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Springer London 01.09.2025
Springer Nature B.V
Témata:
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.
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-023-08287-5