An improved snake optimization algorithm based on hybrid strategy

doi:10.1007/s11227-025-07258-y

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An improved snake optimization algorithm based on hybrid strategy

2025-04-23

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DOI：10.1007/s11227-025-07258-y

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Abstract

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The Snake Optimization algorithm (SO) is an efficient meta-heuristic algorithm. However, it still has insufficient convergence speed and accuracy when tackling complex problems. To address these shortcomings, this paper proposes an improved Snake Optimization algorithm based on Hybrid Strategy (HSO). During the population initialization phase, the study employs a good point set initialization method, resulting in a more uniform distribution of the initial population. Second, a nonlinear balance factor is introduced to better balance exploration and exploitation. Furthermore, the differential evolution strategy and L & eacute;vy flight strategy are introduced to enhance the algorithm's capability to escape local optima. To evaluate the effectiveness of the proposed strategies, this study conducted an ablation comparison experiment based on the CEC2022 benchmark functions and compared the HSO algorithm with several meta-heuristic algorithms. The results of the experiment were then statistically analyzed using the Friedman test and Wilcoxon signed-rank test. Finally, three engineering design problems were employed to assess the application value of HSO in practical problems. The findings demonstrate that HSO achieves significant improvements in optimization capability compared to SO, and outperforms the comparison algorithms.

Keywords:

Snake optimization algorithm

Good point set

Balance factor

L & eacute

vy flight