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mESC:一种增强的逃脱算法,将多种工程优化策略融合在一起.

Jia Liu1, Jianwei Yang2, Lele Cui3

  • 1Faculty of Mechanical Engineering, Shaanxi University of Technology, Hanzhong 723000, China.

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概括
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关键词:
逃生算法算法 逃生算法的元启发式算法.多策略增强版多策略增强版现实化的优化问题.

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科学领域:

  • 计算智能是一种计算智能.
  • 优化算法 优化算法
  • 超启发式计算 超启发式计算

背景情况:

  • 标准逃生算法 (ESC) 在平衡勘探和开发方面面临挑战,导致收精度低.
  • 保持人口多样性和增强勘探能力对于有效的优化算法至关重要.

研究的目的:

  • 提出一个多策略增强逃脱算法 (mESC),解决原来的ESC的局限性.
  • 在优化过程中改善勘探和开发阶段之间的平衡.
  • 提高趋同的准确性和全球趋同的速度.

主要方法:

  • 实施了适应性扰动因子策略,以保持人口多样性.
  • 引入了一个重启机制,以加强算法的探索能力.
  • 设计了一个动态的中心点反向学习策略,以实现均衡的当地发展.
  • 开发了一个边界调整策略,使用精英池来加速全球趋同.

主要成果:

  • 与CEC2022测试套件上最新的元启发式和高性能获胜算法相比,mESC算法表现出更高的性能.
  • 数字结果证实了mESC的增强性能和有效性.
  • 该算法的优越性在几个经典的现实世界优化问题上得到了验证.

结论:

  • 拟议的mESC算法有效地解决了标准ESC的局限性,特别是在平衡勘探和开发方面.
  • mESC表现出更好的收精度和全球收速度.
  • 改进的算法显示了解决复杂的现实世界优化挑战的巨大潜力.