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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

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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.

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2017 年 CEC 和 2022 年 CEC 的基准功能.适应性切换策略 适应性切换策略占主导地位的集团指导指南占主导地位的随机差异搜索搜索的元启发式算法.机器人路径规划 机器人路径规划雪算法 雪算法

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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.