Enhancing Exploration-Exploitation Balance in the Coyote Optimization Algorithmthrough Fuzzy Logic-Guided Coefficient Modulation

In recent years, metaheuristic algorithms have exhibited noteworthy performance inaddressing complex optimization problems. Among these, the Coyote OptimizationAlgorithm (COA) has demonstrated promising results in finding optimal solutions acrossvarious domains. In this work, we extend the capabilities of the COA through theintegration of fuzzy logic, a pivotal step aimed at enhancing the algorithm's adaptabilityand robustness.The infusion of fuzzification techniques into the COA framework offers a substantialadvancement, fostering nuanced decision-making and proactive response mechanismswithin the algorithm. Leveraging this enriched adaptability, the fuzzified COAdemonstrates an improved ability to navigate complex search spaces, striking a balancebetween exploration and exploitation, while mitigating the impact of uncertainty ofteninherent in real-world optimization problems.This article presents a comprehensive study of the fuzzified COA, encompassing thetheoretical framework, implementation strategies, and substantial empirical evaluationsacross diverse benchmark functions. Our results reflect notable enhancements inconvergence speed, solution quality, and resilience to noisy environments, positioningthe fuzzified COA as a compelling algorithmic framework for addressing contemporaryoptimization challenges

Keywords:

Fuzzy Logic , Coyote Optimization Algorithm , Metaheuristic Optimization , Fuzzification