Whale Migrating Algorithm (WMA): A Bio-Inspired Optimization Technique
This work introduces the Whale Migrating Algorithm (WMA), a new bio-inspired metaheuristic optimization method modeled after the migratory behavior of humpback whales. Unlike conventional algorithms, WMA uses a dynamic leader-follower structure and adaptive migration strategies to maintain a strong balance between exploration and exploitation. This approach improves the algorithm’s ability to avoid local optima and achieve fast, accurate convergence.
Researchers rigorously tested WMA on multiple standard benchmark problems, including the CEC-2005, CEC-2014, and CEC-2017 function sets. It also performed well on several constrained engineering design problems. Across all tests, WMA consistently delivered superior accuracy, robustness, and convergence speed when compared to well-known optimization methods like Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), and Grey Wolf Optimizer (GWO).
These results highlight WMA’s effectiveness in solving complex optimization problems across various domains, making it a reliable and scalable solution for real-world applications.
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