Artificial rabbit optimization algorithm based on chaotic mapping and Levy flight improvement

doi:10.19682/j.cnki.1005-8885.2024.1010

中国邮电高校学报(英文) ›› 2024, Vol. 31 ›› Issue (4): 54-69.doi: 10.19682/j.cnki.1005-8885.2024.1010

• Artificial Intelligence • 上一篇下一篇

Artificial rabbit optimization algorithm based on chaotic mapping and Levy flight improvement

吴进,苏正东,高亚琼,冯浩然

西安邮电大学

收稿日期:2023-05-16 修回日期:2023-12-30 出版日期:2024-08-31 发布日期:2024-08-31
通讯作者: 吴进 E-mail:wujin1026@126.com
基金资助:
科技创新2030——“新一代人工智能”重大项目

Artificial rabbit optimization algorithm based on chaotic mapping and Levy flight improvement

Received:2023-05-16 Revised:2023-12-30 Online:2024-08-31 Published:2024-08-31
Contact: Jin WU E-mail:wujin1026@126.com

摘要/Abstract

摘要： An artificial rabbit optimization algorithm based on chaotic mapping and Levy flight improvement is proposed, which has the advantages of good initial population quality and fast convergence compared with the traditional artificial rabbit optimization algorithm, called CLARO. CLARO’s improvement method starts from three aspects: to optimize the quality of the initial population of the algorithm a chaotic mapping is brought in to initialize the population; to avoid the algorithm from falling into local optimum Levy flight is added in the exploration phase and the threshold of energy factor A is optimized to better balance exploration and exploitation. The efficiency of CLARO is tested on a set of 23 benchmark function sets by comparing it with ARO and different meta-heuristics algorithms. At last, the comparison experiments conclude that all three improvement strategies enhance the performance of ARO to some extent, with Levy flight providing the most significant improvement in ARO performance. The experimental results showed that CLARO has better results and faster convergence compared to other algorithms, while successfully addressing the drawbacks of ARO and being able to face more challenging problems.

关键词: CLARO|混沌映射|Levy飞行|收敛性

Abstract: An artificial rabbit optimization algorithm based on chaotic mapping and Levy flight improvement is proposed, which has the advantages of good initial population quality and fast convergence compared with the traditional artificial rabbit optimization algorithm, called CLARO. CLARO’s improvement method starts from three aspects: to optimize the quality of the initial population of the algorithm a chaotic mapping is brought in to initialize the population; to avoid the algorithm from falling into local optimum Levy flight is added in the exploration phase and the threshold of energy factor A is optimized to better balance exploration and exploitation. The efficiency of CLARO is tested on a set of 23 benchmark function sets by comparing it with ARO and different meta-heuristics algorithms. At last, the comparison experiments conclude that all three improvement strategies enhance the performance of ARO to some extent, with Levy flight providing the most significant improvement in ARO performance. The experimental results showed that CLARO has better results and faster convergence compared to other algorithms, while successfully addressing the drawbacks of ARO and being able to face more challenging problems.

Key words: CLARO| chaotic mapping| Levy flight| convergence

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Artificial rabbit optimization algorithm based on chaotic mapping and Levy flight improvement

Artificial rabbit optimization algorithm based on chaotic mapping and Levy flight improvement

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