Abstract

This paper presents a novel hybrid metaheuristic algorithm designed to address the challenges posed by high-dimensional global optimization problems. The algorithm synergistically combines the strengths of Particle Swarm Optimization (PSO) and Differential Evolution (DE) with an adaptive control mechanism to dynamically balance exploration and exploitation. The hybrid approach leverages PSO's efficient global search capability and DE's effective local refinement to achieve enhanced performance. The adaptive control mechanism adjusts the contributions of PSO and DE based on the search progress, promoting exploration in the early stages and intensifying exploitation as the search converges. The performance of the proposed algorithm is evaluated on a suite of benchmark functions, including unimodal, multimodal, and composite functions, and compared against established metaheuristic algorithms. The results demonstrate the superior performance of the hybrid algorithm in terms of solution accuracy, convergence rate, and robustness, particularly in high-dimensional spaces.