MAROAOA: A joint opposite selection-based arithmetic artificial rabbits optimization algorithm for solving engineering problems

Yaning Xiao, Ruba Abu Khurma, Hao Cui, David Camacho

Abstract: Artificial Rabbits Optimization (ARO) and Arithmetic Optimization Algorithm (AOA) are two recently proposed metaheuristic techniques that have demonstrated significant potential across various optimization tasks. While ARO exhibits favorable optimization performance, its insufficient local exploitation capability results in slow convergence speed, poor population diversity, and premature convergence when addressing complex optimization challenges. On the other hand, AOA boasts effective local search abilities but suffers from relatively weak global exploration, making it susceptible to getting trapped in local optima. To address these limitations, this study proposes a joint opposite selection-based arithmetic artificial rabbits optimization algorithm (MAROAOA) to provide higher-quality solutions for complex global optimization problems. First, the mathematical frameworks of ARO and AOA are integrated through a random switching hybrid operator, ensuring robust exploration and exploitation capabilities. Second, Lévy flight is incorporated into the initialization phase to enhance population diversity, which contributes to accelerating convergence and broadening the search space. Finally, the joint opposite selection (JOS) strategy is employed to further balance exploration & exploitation, thereby mitigating the risk of getting stuck in local optima. To verify the effectiveness of MAROAOA, the proposed method is benchmarked against several state-of-the-art metaheuristic algorithms and enhanced variants on the IEEE CEC2017 and CEC2019 test suites. Statistical results suggest that MAROAOA outperforms other competitor methods in most test cases in terms of solution accuracy, convergence speed, and stability. Specifically, MAROAOA achieves the lowest Friedman mean ranks of 1.4828 and 1.5000 on the two test suites, confirming its excellent comprehensive optimization performance. In addition, the applicability of MAROAOA is verified by solving five constrained engineering design cases and the parameter identification problem of photovoltaic (PV) systems, highlighting its good potential for real-world optimization applications.

External IDs:dblp:journals/cluster/XiaoKCC25