A study on multi-strategy improvement algorithm for chimpanzee optimization by fusing differential variation and random variation

Dan-dan Li; Zhong-kang Li; Hong Wang

doi:10.1117/12.2684521

1 August 2023 A study on multi-strategy improvement algorithm for chimpanzee optimization by fusing differential variation and random variation

Dan-dan Li, Zhong-kang Li, Hong Wang

Author Affiliations +

Proceedings Volume 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023); 127542X (2023) https://doi.org/10.1117/12.2684521
Event: 2023 3rd International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 2023, Hangzhou, China

Abstract

To address the problems that the chimpanzee optimization algorithm is prone to fall into local optimum and convergence speed is too slow. This paper proposes a multi-strategy improvement of the chimpanzee optimization algorithm by fusing differential variation and random variation (DRChOA). Firstly, an adaptive weighting factor is introduced to balance the global search and local exploitation ability of the algorithm, secondly, the differential variation strategy is introduced to facilitate the exchange of information among individuals for population position updating, and finally, the random variation strategy is used to enhance the ability of the algorithm to jump out of the local optimum.

Citation Download Citation

Dan-dan Li, Zhong-kang Li, and Hong Wang "A study on multi-strategy improvement algorithm for chimpanzee optimization by fusing differential variation and random variation", Proc. SPIE 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 127542X (1 August 2023); https://doi.org/10.1117/12.2684521

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