Generalized Type-2 Fuzzy Logic-Enhanced Mayfly Algorithm for Robust and Adaptive Optimization

Abstract

The Mayfly Algorithm (MA) has demonstrated great potential as a metaheuristic for solving complex optimization problems. In this work, we propose the integration of generalized Type-2 fuzzy logic for dynamic parameter adaptation in MA, enabling accurate tuning under uncertain conditions. The proposed method improves convergence, robustness, and solution quality, making it suitable for a wide variety of applications, ranging from engineering and bioinformatics to systems biology and computational modeling. Comparative evaluation with mathematical benchmark functions demonstrates that the incorporation of generalized Type-2 fuzzy logic in MA outperforms traditional variants, highlighting its versatility and effectiveness in addressing diverse and complex optimization challenges.