Abstract
Coyote Optimization Algorithm (COA) is classified as both swarm intelligence and evolutionary heuristic algorithms. However, getting trapped in a poor local optimum and the low convergence speed are the weaknesses of COA obviously. Due to these weaknesses, this paper proposes a new algorithm named Chaotic Coyote Optimization Algorithm (CCOA) which focusing on COA equipped with chaotic maps. Through utilising ten well-known benchmark functions, experimental results are recorded in tables and drawn in figures to provide a sharp contrast. The performance of CCOA and COA are discussed, which proves CCOA outperforms COA guaranteeing rapid global convergence rate.
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Acknowledgements
This work was supported by grants from the National Key Research and Development Program of China under Contract No. 2016YFE0200200, the National Natural Science Funds of China under Contract No. 61701253 and 61801240.
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Tong, H., Zhu, Y., Pierezan, J. et al. Chaotic Coyote Optimization Algorithm. J Ambient Intell Human Comput 13, 2807–2827 (2022). https://doi.org/10.1007/s12652-021-03234-5
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DOI: https://doi.org/10.1007/s12652-021-03234-5