一个增强的雪优化器,集成多种数值优化策略
Baoqi Zhao1,2, Yu Fang3, Tianyi Chen4
1Institute of Artificial Intelligence Application, Ningbo Polytechnic, Ningbo 315800, China.
Biomimetics (Basel, Switzerland)
|June 25, 2025
概括
增强的雪算法 (ESGA) 改善了种群多样性和搜索平衡. 实验表明ESGA的性能优于其他算法,并有效地解决复杂的问题,如机器人路径规划.
科学领域:
- 人工智能的人工智能
- 优化算法 优化算法
- 计算智能是一种计算智能.
背景情况:
- 标准的雪算法 (SGA) 受到有限的种群多样性和不平衡的搜索趋势的影响.
- 这些局限性阻碍了它在复杂的优化任务中的有效性.
研究的目的:
- 引入一个增强的雪算法 (ESGA),解决标准SGA的缺陷.
- 改善人口多样性,平衡勘探和开发,增强逃离当地最佳条件的能力.
主要方法:
- 采用了适应性切换策略,以动态平衡开采和勘探.
- 引入了一种占主导地位的群体指导策略,以提高总体人口质量.
- 一个主导的随机差异搜索策略旨在丰富多样性,并促进逃离局部最佳.
- 在CEC2017测试组上的废弃实验验证了个别改进的有效性.
- 对CEC2022测试套件的比较研究将ESGA与最先进的算法进行了比较.
主要成果:
- 废弃性研究证实了每个建议策略对算法的性能有积极的贡献.
- 与CEC2022测试套件中备受引用和强大的改进算法相比,ESGA表现优越.
- 该算法成功解决了复杂的机器人路径规划问题,展示了其实际适用性.
结论:
- 拟议的ESGA通过增强人口多样性和搜索能力,有效地克服了标准SGA的局限性.
- ESGA提供了一种强大而高效的优化方法,在基准测试函数上表现优于现有的方法.
- 该算法显示了解决现实世界复杂问题的巨大潜力,包括路径规划.
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