文献类型： 外文期刊

第一作者： Xiang, Bao-Wei

作者： Xiang, Bao-Wei;Xiang, Yi-Xin;Xiang, Yi-Xin;Zhang, Tian-Yi

作者机构：

关键词： Meta-heuristic algorithm Rabbit Optimizer; Foraging tactic; Engineering optimization; Large scale optimization

期刊名称：APPLIED MATHEMATICAL MODELLING （ 影响因子：5.1； 五年影响因子：4.6 ）

ISSN： 0307-904X

年卷期： 2025 年 140 卷

页码：

收录情况： SCI

摘要： In this paper, a novel bio-inspired meta-heuristic algorithm, namely Rabbit Optimizer, is proposed. It draws inspiration from how rabbits survive in nature through foraging tactics, random hiding and rapid growth. The foraging strategy involves rabbits avoiding grazing near their dens, thus preventing their dens from being discovered. Random hiding refers to cunning rabbits maintaining three dens to reduce the chance of being eliminated. Additionally, rapid growth and strong reproductive abilities enable rabbits to thrive as the fittest species. Rabbit Optimizer employs three subgroups of male, female and child rabbits to conduct an efficient collaborative search under an elite learning strategy, achieving a good balance of exploration and exploitation. Key parameters like distance factor, balance factor and learning factor strike a balance between depth search and width searches, making Rabbit Optimizer versatile and flexible. The performance of Rabbit Optimizer was evaluated by handling the CEC2022 and various dimensions of the CEC05 test suites up to 5000. Using Friedman analysis, Rabbit Optimizer ranked first and was proven to be significantly superior to 13 well-known metaheuristic algorithms. Finally, it addressed ten problems from the CEC2020 to demonstrate its ability to solve real-world engineering optimization problems. Experimental results show that Rabbit Optimizer is effective and efficient for solving complex large-scale optimization problems.

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