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

doi:10.19682/j.cnki.1005-8885.2024.1010

The Journal of China Universities of Posts and Telecommunications ›› 2024, Vol. 31 ›› Issue (4): 54-69.doi: 10.19682/j.cnki.1005-8885.2024.1010

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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

Abstract

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

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