Abstract
In this research, we have conducted an in-depth exploration of the integration of the Black-winged kite algorithm (BKA) and the Artificial Rabbit optimization (ARO). This fusion draws from the strengths of both algorithms, offering a powerful tool for addressing complex problems. We have employed a master-slave model strategy, introducing a master-slave structure during the optimization process to enhance search efficiency and optimize algorithm performance. Furthermore, we introduced a strategy known as the good point set for the initialization of the population. This strategy helps prevent the algorithm from falling into local optimal solutions, thereby enhancing the universality and accuracy in problem-solving. We tested the performance of the algorithm on 8 benchmark functions. Experimental results demonstrate that our improved fusion strategy has superior comprehensive performance over advanced optimization algorithms such as the Artificial Rabbit optimization, implying evident superiority and application potential of our strategy.
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Acknowledgments
This work is supported by the National Natural Science Foundation (62376045,12201089), the Natural Science Foundation of Chongqing (cstc2022ycjh-bgzxm0004, cstc2019jcyj-cxttX0002, cstc2021ycjh-bgzxm0013, cstb2022nscq-msx0226), the Key Cooperation Project of Chongqing Municipal Education Commission (HZ2021008), the Science and Technology Research Program of Chongqing Education Commission of China (KJQN202200 513), the Innovation Projects for Studying Abroad and Returning to China (cx2023097).
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Xue, R., Zhang, X., Xu, X., Zhang, J., Cheng, D., Wang, G. (2024). Multi-strategy Integration Model Based on�Black-Winged Kite Algorithm and�Artificial Rabbit Optimization. In: Tan, Y., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2024. Lecture Notes in Computer Science, vol 14788. Springer, Singapore. https://doi.org/10.1007/978-981-97-7181-3_16
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DOI: https://doi.org/10.1007/978-981-97-7181-3_16
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