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 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.