An artificial rabbit optimization(ARO)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 tradi...An artificial rabbit optimization(ARO)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 ARO algorithm,called CLARO.CLARO is improved by applying three methods.Chaotic mapping is introduced,which can optimize the quality of the initial population of the algorithm.Add Levy flight in the exploration phase,which can avoid the algorithm from falling into a local optimum.The threshold of the energy factor is optimized,which can 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 show 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.展开更多
基金National Key R&D Program of China:Science and Technology Innovation 2030(2022ZD0119001).
文摘An artificial rabbit optimization(ARO)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 ARO algorithm,called CLARO.CLARO is improved by applying three methods.Chaotic mapping is introduced,which can optimize the quality of the initial population of the algorithm.Add Levy flight in the exploration phase,which can avoid the algorithm from falling into a local optimum.The threshold of the energy factor is optimized,which can 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 show 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.
