| 摘 要: 针对野马优化算法收敛慢、寻优精度低等不足,提出一种基于黄金正弦指引的扰动野马优化算法(GPWHO)。GPWHO 算法通过黄金分割系数加快算法的收敛;通过加入扰动因子策略平衡算法的全局探索和局部开发能力。选取13个基准测试函数进行测试,实验结果表明,GPWHO 算法相比其他5种对比算法,在单峰函数F1~F5上均能稳定寻得理论最优值0,在多峰函数F7~F11的求解结果也有不同数量级上的提高,说明 GPWHO算法具有更好的收敛精度和全局探索能力。 |
| 关键词: 野马优化算法 黄金正弦 扰动因子 秩和检验 数值实验 |
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中图分类号: TP301.6
文献标识码: A
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| 基金项目: 国家自然科学基金资助项目(12361106);贵州省自然科学基金重点项目(黔科合基础-ZK[2023]重点003);中央支持地方科研创新团队项目(PXM2013_014210_000173);北京建筑大学2021年校级教育科学研究项目(Y2113) |
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| GoldenSine-GuidedDisturbed WildHorseOptimization AlgorithmandItsApplication |
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WANG Minhao, LIANG Ximing
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(School of Science, Beijing University of Civil Engineering and Architecture, Beijing 102616, China)
bucea_wmh@163.com; liangximing@bucea.edu.cn
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| Abstract: To address the shortcomings of the Wild Horse Optimization (WHO), such as slow convergence speed and low optimization accuracy, a Golden Sine-Guided Disturbed Wild Horse Optimization (GPWHO) is proposed. The GPWHO algorithm accelerates convergence speed through the golden section coefficient and balances global exploration and local exploitation capabilities by incorporating a disturbance factor strategy. Thirteen benchmark test functions were selected for evaluation. Experimental results demonstrate that, compared to five other comparative algorithms, GPWHO consistently achieves the theoretical optimal value of 0 for unimodal functions F1 to F5. Additionally, the results for multimodal functions F7 to F11 show improvements of varying orders of magnitude,
indicating that GPWHO exhibits superior convergence accuracy and global exploration capabilities. |
| Keywords: wild horse optimization algorithm golden sine disturbance factor rank-sum test numerical experiment |
