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GWOA:用于工程设计优化的多策略增强型鱼优化算法

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概括
此摘要是机器生成的。

改进的鱼优化算法 (GWOA) 提高了复杂问题的全球搜索和融合. 在基准和工程优化任务中,GWOA表现出卓越的性能,提供更稳定,更高效的解决方案.

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科学领域:

  • 计算智能
  • 优化算法
  • 超听觉学

背景情况:

  • 传统的鱼优化算法 (WOA) 存在局部优化,缓慢的融合,以及对高维问题有限的全球搜索.
  • 现有的WOA变种在复杂的优化场景中可能无法完全解决这些限制.

研究的目的:

  • 提出一个改进的鱼优化算法 (GWOA),解决传统的WOA的缺陷.
  • 提高复杂优化问题的全球搜索能力,融合速度和解决准确性.

主要方法:

  • 将适应性参数调整,增强猎物包围和正弦搜索策略集成到WOA中.
  • 在23个基准功能 (单模,多模,组合) 和工程设计问题 (压力容器,弹) 上测试GWOA.
  • 使用基本的元启发分析,WOA变体和最先进的算法进行比较分析,包括可扩展性实验.

主要成果:

  • 在大多数基准函数中,GWOA表现出卓越的融合速度和解决方案准确性,特别是在多式联络和组合问题上.
  • 在基准测试中达到74.46%的整体效率 (OE).
  • 有效地降低了成本,并满足了工程优化问题的限制,显示了增强的稳定性和优化能力.

结论:

  • 通过多策略集成,GWOA显著提高了全球搜索,融合速度和解决方案稳定性.
  • GWOA显示出解决复杂优化问题的巨大潜力,并作为工程应用的高效工具.
  • 拟议的GWOA为传统的优化算法提供了强大而有效的替代方案.