An Improved Attraction-Repulsion OptimizationAlgorithm for Global Optimization and EngineeringDesign Problems

Abstract

Attraction-Repulsion Optimization Algorithm (AROA) is a recently proposed meta-heuristic algorithm known for its simplicity, ease of implementation, and robustness. However, AROA may converge to local optima when applied to complex optimization problems. To address this limitation, we propose an enhanced version called the Differential Cauchy Tangent Attraction-Repulsion Optimization Algorithm (DCTAROA). First, we propose a mutation operator based on a tangent flight mutation strategy and a dimension decision mechanism using the inverse cumulative distribution function of the Cauchy distribution. The tangent flight mutation enhances the local search capability and accelerates convergence, while the dimension decision strategy of the Cauchy distribution inverse cumulative function increases population diversity and improves exploration efficiency. Subsequently, we integrate Differential Evolution (DE) as a local search mechanism to strengthen the global optimization performance of AROA. To evaluate the proposed algorithm, we compare it with 15 state-of-the-art algorithms on 29 CEC2017 benchmark functions across various dimensions. Experimental results demonstrate that DCTAROA outperforms the compared algorithms in terms of solution accuracy, stability, convergence speed, and statistical significance based on the Wilcoxon rank-sum test. Furthermore, we apply DCTAROAto three practical engineering design problems. The results confirm that DCTAROA effectively explores the search space and yields competitive solutions, thereby validating its practical applicability.