Fick’s Law Algorithm Enhanced with Opposition-Based Learning

Metaheuristic algorithms are widely used for solving complex optimization problems without relying on gradient information. They efficiently explore large, non-convex, and high-dimensional search spaces but face challenges with dynamic environments, multi-objective goals, and complex constraints. This paper introduces a novel hybrid algorithm, Fick’s Law Algorithm with Opposition-Based Learning (FLA-OBL), combining the FLA’s strong exploration–exploitation balance with OBL’s enhanced solution search. Tested on CEC2017 benchmark functions, FLA-OBL outperformed state-of-the-art algorithms, including the original FLA, in convergence speed and solution accuracy. To address real-world multi-objective problems, we developed FFLA-OBL (Fuzzy FLA-OBL) by integrating a fuzzy logic system for UAV path planning with obstacle avoidance. This variant effectively balances exploration and exploitation in complex, dynamic environments, providing efficient, feasible solutions in real time. The experimental results confirm FFLA-OBL’s superiority over the original FLA in both solution optimality and computational efficiency, demonstrating its practical applicability for multi-objective optimization in UAV navigation and related fields.