An oppositional learning and chaotic local search-based artificial electric field algorithm for engineering optimization
摘要
Metaheuristic algorithms are stochastic optimization techniques inspired by natural phenomena, with their performance driven by two key operators: exploration and exploitation. Despite their success, a common limitation is their susceptibility to premature convergence and slow progress toward optimal solutions. To address these challenges, this study presents an innovative extension of the Artificial Electric Field Algorithm (AEFA), a method rooted in charged system search principles. The proposed algorithm incorporates oppositional-based learning (OBL) and an elite chaotic local search mechanism to enhance both global and local search capabilities. OBL improves the exploration of the global search space, while the elite chaotic local search focuses on refining solutions around near-optimal regions, ensuring accelerated convergence. This hybrid approach is tested on CEC2020 bound-constrained numerical optimization problems and real-world applications such as topology optimization and gear train design. The findings demonstrate the superiority of the proposed algorithm in terms of achieving better optimal solutions, faster convergence rates, and reduced algorithmic complexity when compared to existing state-of-the-art algorithms. Some notable performance of the proposed algorithm are, it demonstrates superior performance over all listed algorithms for 10D problems