A Hybrid Enhanced Mayfly Optimization Algorithm with Improved Performance through Fuzzy-Based Automatic Parameter Adaptation

Abstract

Inspired by the unique behavioral patterns of mayflies, characterized by their brief lifespans and complex mating dynamics, the Mayfly algorithm represents a novel and effective optimization approach. Rooted in the principles of particle swarm optimization, this algorithm combines swarm intelligence with evolutionary mechanisms to achieve enhanced performance in solving computational problems. This study focuses on improving the Mayfly algorithm through the adaptive adjustment of its parameters, leveraging fuzzy logic for stability in exploration and exploitation. The proposed adaptation enhances the algorithm’s capability to address optimization tasks, demonstrating superior performance in convergence speed and solution reliability. Simulation results show the advantages of the hybrid approach.