The Artificial Rabbit Optimization (ARO) algorithm has been proposed as an effective metaheuristic optimization approach in recent years. However, the ARO algorithm exhibits shortcomings in certain cases, including inefficient search, slow convergence, and vulnerability to local optima. To address these issues, this paper introduces a multi-strategy improved Artificial Rabbit Optimization (IARO) algorithm. Firstly, in the enhanced search strategy, we propose integrating the centroid guidance mechanism and elite guidance mechanism with the greedy strategy to update the position during the exploration phase. Additionally, the Levy flight strategy integrated with self-learning, is employed to update the position during the development phase to improve convergence speed and prevent falling into local optima. Secondly, the algorithm incorporates a per-dimension mirror boundary control strategy to map individuals exceeding the boundary back within the boundary back inside the boundary. This boundary control strategy ensures the algorithm operates within bounds and enhances convergence speed. Finally, within the survival of the fittest strategy, an adaptive factor is introduced to gradually enhance the population's overall adaptability. This factor regulates the balance between exploration and exploitation, allowing the algorithm to fully explore the search space and improve its robustness. To substantiate the effectiveness of the proposed IARO algorithm, a rigorous and systematic verification analysis was undertaken. Comparative experiments for qualitative and quantitative analysis were conducted on three benchmark test sets, namely CEC2017, CEC2020, and CEC2022. The analysis results, including the Wilcoxon rank-sum test, consistently demonstrates that this improved algorithm outperforms ARO and other state-of-the-art optimization algorithms comprehensively. Finally, the feasibility of the IARO algorithm has been verified in seven classical constrained engineering problems.

中文翻译：

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基于融合质心和精英引导机制的多策略改进人工兔优化算法

近年来，人工兔优化（ARO）算法被提出作为一种有效的元启发式优化方法。然而，ARO算法在某些情况下表现出一些缺点，包括搜索效率低、收敛速度慢以及容易陷入局部最优。为了解决这些问题，本文引入了一种多策略改进的人工兔优化（IARO）算法。首先，在增强搜索策略中，我们提出将质心引导机制和精英引导机制与贪婪策略相结合，以在探索阶段更新位置。此外，采用与自学习相结合的Levy飞行策略在开发阶段更新位置，以提高收敛速度并防止陷入局部最优。其次，该算法结合了每维镜像边界控制策略，将超出边界的个体映射回边界内。这种边界控制策略确保算法在边界内运行并提高收敛速度。最后，在优胜劣汰策略中引入适应性因素，逐步增强种群的整体适应能力。该因素调节探索和利用之间的平衡，使算法能够充分探索搜索空间并提高其鲁棒性。为了证实所提出的 IARO 算法的有效性，进行了严格且系统的验证分析。在CEC2017、CEC2020、CEC2022这三个基准测试集上进行了定性和定量分析的对比实验。包括 Wilcoxon 秩和检验在内的分析结果一致表明，该改进算法全面优于 ARO 和其他最先进的优化算法。最后，在7个经典约束工程问题中验证了IARO算法的可行性。