Abstract
The Mayfly Optimization Algorithm (MO), inspired by the swarming and mating behavior of adult mayflies, has emerged as a powerful tool for tackling optimization problems, particularly those seeking minimum values. This review paper delves into the core principles of MO, exploring its stages of swarming, velocity update, movement, mating, and selection. We analyze the strengths of MO, including its ability to balance exploration and exploitation during the search process, leading to well-converged solutions. Additionally, the paper examines recent advancements in MO that address potential limitations. We discuss how incorporating techniques like greedy selection and auto-termination can enhance the convergence speed and efficiency of the algorithm. Furthermore, the review explores various applications of MO across diverse fields, highlighting its potential for solving real-world minimization problems. Finally, we identify and discuss ongoing research directions, including hybridization with other algorithms and exploration of advanced termination strategies. This review paper aims to provide a comprehensive understanding of the Mayfly Optimization Algorithm, its capabilities, and promising areas for future development. Index Terms— Mayfly optimization, particle swarm optimization, swarm intelligence, nature inspired algorithm.
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