Recent advances in multi-objective whale optimization algorithm, its versions and applications

Multi-objective optimization (MO) addresses problems involving multiple conflicting objectives, requiring effective techniques to identify Pareto optimal solutions. Among the numerous MO approaches, the Multi-Objective Whale Optimization Algorithm (MOWOA) has emerged as a robust metaheuristic inspir...

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Bibliographic Details
Published in:Journal of King Saud University. Computer and information sciences Vol. 37; no. 7; pp. 200 - 38
Main Authors: Makhadmeh, Sharif Naser, Kassaymeh, Sofian, Rjoub, Gaith, Bataineh, Bilal, Sanjalawe, Yousef, Al-Betar, Mohammed Azmi
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.09.2025
Springer Nature B.V
Springer
Subjects:
Algorithms
Bibliometrics
Computer Imaging
Computer Science
Database Management
Engineering
Heuristic methods
Machine Learning
Metaheuristics
Multi-objective optimization
Multi-objective whale optimization algorithm
Multiple objective analysis
Optimization algorithms
Pareto optimization
Pareto optimum
Pattern Recognition and Graphics
Review Paper
Robotics
Science
Software Engineering/Programming and Operating Systems
Systems and Data Security
Theory of Computation
Vision
China
Metaheuristics
Multi-objective optimization
Multi-objective whale optimization algorithm
ISSN:1319-1578, 2213-1248, 1319-1578
Online Access:Get full text
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Summary:Multi-objective optimization (MO) addresses problems involving multiple conflicting objectives, requiring effective techniques to identify Pareto optimal solutions. Among the numerous MO approaches, the Multi-Objective Whale Optimization Algorithm (MOWOA) has emerged as a robust metaheuristic inspired by the bubble-net hunting strategy of humpback whales. This algorithm excels in solving optimization problems by combining global and local search capabilities through encircling prey, spiral bubble-net attacks, and random search mechanisms. This paper provides an in-depth review of MOWOA, examining its theoretical foundation, evolution, variations, and applications across various domains. Additionally, the review critically evaluates MOWOA’s strengths, including effective diversity maintenance and leader selection, as well as its limitations when addressing large-scale problems.
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ISSN:1319-1578
2213-1248
1319-1578
DOI:10.1007/s44443-025-00184-2