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一个改进的灰狼优化算法解决功能优化和工程设计问题.

Yihui Qiu1, Xiaoxiao Yang1, Shuixuan Chen2,3

  • 1School of Economics and Management, Xiamen University of Technology, Xiamen, 361024, China.

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概括
此摘要是机器生成的。

本研究引入了改进的灰狼优化器 (IGWO),以提高优化速度和准确性. 这种新的算法在解决复杂的工程问题上表现出卓越的性能.

关键词:
工程设计优化工程设计优化勘探和开采活动功能优化优化 功能优化灰狼算法是什么?灰狼算法是什么?

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科学领域:

  • 计算智能是一种计算智能.
  • 超启发式优化优化
  • 工程应用 工程应用

背景情况:

  • 灰狼优化器 (GWO) 是一个受欢迎的元启发算法,灵感来自狼群行为.
  • 传统的GWO在融合速度,溶液精度和逃避局部最小值方面面临限制.
  • 需要进行改进,以扩大GWO在复杂工程优化中的适用性.

研究的目的:

  • 为了提出一个多策略的融合改进了灰狼优化器 (IGWO).
  • 为了提高GWO的融合速度,解决方案的准确性,以及逃避局部最小值的能力.
  • 评估IGWO在基准和工程问题上的表现.

主要方法:

  • 实现了镜头成像反向学习,用于初始人口优化.
  • 引入了一个基于非线性等号变化控制参数策略.
  • 集成的非线性参数调整和位置校正,灵感来自Tunicate Swarm算法 (TSA) 和粒子群集优化 (PSO).

主要成果:

  • 国际勘探与开发组织 (IGWO) 证明了勘探和开发 (E&P) 能力的提高.
  • 统计分析 (威尔科克森等级和,弗里德曼测试) 证实了IGWO的平衡表现.
  • 在测试的问题上,IGWO在现有最先进的算法上显示出了显著的优势.

结论:

  • 拟议的IGWO算法有效地解决了传统GWO的局限性.
  • 对于工程领域的各种优化挑战,IGWO提供了强大而高效的解决方案.
  • 多策略融合方法在元启发性优化方面取得了重大进展.