Fusion strategy-driven dung beetle optimization algorithm

Xueyu Huang; Jinwei Yi

doi:10.1117/12.3051352

18 November 2024 Fusion strategy-driven dung beetle optimization algorithm

Xueyu Huang, Jinwei Yi

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 134030Q (2024) https://doi.org/10.1117/12.3051352
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

Considering the challenges posed by the dung beetle optimization algorithm, which often gravitates towards local optimality and disproportionate global exploration and local development potentials, a fusion strategy driven dung beetle optimization algorithm (FSDDBO) is proposed. Firstly, the piecewise linear chaotic map (PWLCM) is used to initialize the population, so that the dung beetle population can better traverse the entire solution space; secondly, an improved spiral search strategy is added in the dung beetle breeding stage to accelerate the convergence speed and improve individual diversity; finally, a competitive reverse learning strategy is added in the dung beetle stealing stage to increase the randomness of the dung beetle and update the existing worldwide most unfavorable solution. In 11 benchmark function tests and comparisons with other algorithms, the method proposed in this research improves the algorithm's capacity to leap beyond its local optimum, ensuring strong robustness and precision in optimization, achieving good results.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xueyu Huang and Jinwei Yi "Fusion strategy-driven dung beetle optimization algorithm", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 134030Q (18 November 2024); https://doi.org/10.1117/12.3051352

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