Group-based whale optimization algorithm

Meta-heuristic algorithms are divided into two categories: biological and non-biological. Biological algorithms are divided into evolutionary and swarm-based intelligence, where the latter is divided into imitation based and sign based. The whale algorithm is a meta-heuristic biological swarm-based...

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Veröffentlicht in:Soft computing (Berlin, Germany) Jg. 24; H. 5; S. 3647 - 3673
Hauptverfasser: Hemasian-Etefagh, Farinaz, Safi-Esfahani, Faramarz
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2020
Springer Nature B.V
Schlagworte:
Artificial Intelligence
Chaos theory
Computational Intelligence
Control
Convergence
Engineering
Evolutionary algorithms
Exploitation
Heuristic
Heuristic methods
Intelligence
Mathematical Logic and Foundations
Mechatronics
Methodologies and Application
Methods
Optimization algorithms
Particle swarm optimization
Robotics
Whales & whaling
Meta-heuristic algorithm
Biological
Whale optimization algorithm
ISSN:1432-7643, 1433-7479
Online-Zugang:Volltext
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Zusammenfassung:Meta-heuristic algorithms are divided into two categories: biological and non-biological. Biological algorithms are divided into evolutionary and swarm-based intelligence, where the latter is divided into imitation based and sign based. The whale algorithm is a meta-heuristic biological swarm-based intelligence algorithm (based on imitation). This algorithm suffers from the early convergence problem which means the population convergences early to an unfavorable optimum point. Usually, the early convergence occurs because of the weakness in exploration capability (global search). In this study, an optimized version of the whale algorithm is proposed that introduces a new idea in grouping of whales (called GWOA) to overcome the early convergence problem. The proposed whale optimization algorithm is compared with the standard whale algorithm (WOA), CWOA improved whale algorithm, particle swarm optimization, and BAT algorithms applying CEC2017 functions. The results of the experiments show that the proposed method applying Friedman’s test on 30 standard benchmark functions has a better performance than the other baseline algorithms.
Bibliographie:ObjectType-Article-1
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-019-04131-y