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    Advances in Artificial Rabbits Optimization: A Comprehensive Review
    (Springer Science and Business Media B.V., 2024) Anka, Ferzat; Ağaoğlu, Nazım; Nematzadeh, Sajjad; Torkamanian afshar, Mahsa; Gharehchopogh, Farhad Soleimanian
    This study provides an in-depth review and analysis of the Artificial Rabbit Optimization (ARO) algorithm inspired by the survival strategies of rabbits. The ARO tries to find the global solution in the search space according to the rabbits’ detour foraging strategy and searches locally according to their random hiding structure. This algorithm has various advantages such as a simple structure, fast running model, easy adaptation feature, few parameters, independent mechanism in exploration and exploitation phases, transitions between phases with a specific mechanism, reasonable convergence rate, and property of escaping local optima. Therefore, it has been preferred by many researchers to solve various complex optimization problems. ARO-based studies have been published at prestigious international publishers such as Elsevier, Springer, MDPI, and IEEE since its launch in July 2022. The rates of studies in these publishers are 34%, 19%, 18%, and 15%, respectively. The remaining 14% includes papers published by other publishers. Besides, the cited studies on this algorithm are examined in four categories: Improved, hybrid, variants, and adapted. Research trends demonstrate that 27%, 31%, 9%, and 33% of ARO-based studies fall into these categories. © The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE) 2024.