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混合鱼优化与火虫算法用于功能优化和移动机器人路径规划.

Tao Tian1, Zhiwei Liang2,3,4, Yuanfei Wei5

  • 1College of Economics, Guangxi Minzu University, Nanning 530006, China.

Biomimetics (Basel, Switzerland)
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概括
此摘要是机器生成的。

本研究介绍了用于移动机器人路径规划 (MRPP) 的混合火-鱼优化算法 (FWOA). 与其他元启发算法相比,FWOA在找到最佳路径方面表现出卓越的表现.

关键词:
火虫算法是一种算法.混合元启发算法混合元启发算法移动机器人的路径规划多种人口的多种人口.基于对立的学习.鱼优化算法 鱼优化算法

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科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 人工智能的人工智能
  • 优化算法 优化算法

背景情况:

  • 移动机器人路径规划 (MRPP) 对自主系统至关重要.
  • 超听觉算法,特别是基于群体的算法,对于解决MRPP.有效.
  • 现有的算法可能会在复杂的环境中面临挑战,并平衡勘探/开发.

研究的目的:

  • 为移动机器人路径规划开发一个改进的元启发算法.
  • 在复杂的环境中提高解决方案的准确性和融合速度.
  • 引入一种混合方法,将鱼优化算法 (WOA) 和火虫算法 (FA) 结合起来.

主要方法:

  • 提出一个混合蝶鱼优化算法 (FWOA).
  • 将多人群和基于对立的学习纳入FWOA.
  • 在优化和MRPP的23个基准函数上测试FWOA.

主要成果:

  • 与其他十个经典的元启发算法相比,FWOA表现出了优越的性能.
  • 鱼优化算法 (WOA) 组件显示出了显著的融合速度和探索能力.
  • FWOA被证明是一个强大的竞争对手对最先进的元启发算法.

结论:

  • 拟议的FWOA有效地优化了移动机器人路径规划.
  • 在复杂的环境中,FWOA提供了一种强大的解决方案,用于在复杂环境中找到最佳路径.
  • 混合方法平衡勘探和开采,从而提高性能.