An improved dingo optimization algorithm based on periodic convergence factor strategy

Huiling Xu; Sheng Liu

doi:10.1117/12.2656879

13 January 2023 An improved dingo optimization algorithm based on periodic convergence factor strategy

Huiling Xu, Sheng Liu

Proceedings Volume 12510, International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022); 125101D (2023) https://doi.org/10.1117/12.2656879
Event: International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022), 2022, Qingdao, China

Abstract

Aiming at the shortcomings of Dingo Optimization Algorithm (DOA), such as low precision, less diversity of population and weak global searching ability, an improved dingo optimization algorithm based on periodic convergence factor strategy is proposed. The convergence factor is incorporated into the location update of group attack, persecution attack and sweep of the basic DOA, and the convergence speed and accuracy of the algorithm are obviously improved. By solving the low-dimensional and Galway test functions, the experimental results verify the optimization performance of the improved algorithm, which can be concluded that IDOA has stronger optimization ability and faster convergence speed.

Citation Download Citation

Huiling Xu and Sheng Liu "An improved dingo optimization algorithm based on periodic convergence factor strategy", Proc. SPIE 12510, International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022), 125101D (13 January 2023); https://doi.org/10.1117/12.2656879

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