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)
|January 22, 2024
概括
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.
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.
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