一个智能增强的群优化算法用于工程应用中移动机器人的全球路径规划
Peng Li1,2, Lei Wei1, Dongsu Wu3
1College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China.
Sensors (Basel, Switzerland)
|March 17, 2025
概括
本研究介绍了一种智能增强的群优化 (IEACO) 算法,以改善移动机器人的全球路径规划. 通过整合六个创新策略,IEACO提高了搜索效率并优化了路径.
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:
- 全球路径规划对于移动机器人导航至关重要.
- 殖民地优化 (ACO) 是一种常见的蜂群智能方法.
- 标准ACO在效率和过早融合方面存在局限性.
研究的目的:
- 提出一个智能增强的殖民地优化 (IEACO) 算法.
- 在移动机器人路径规划中解决传统ACO的局限性.
- 提高搜索效率,勘探开发平衡和路径质量.
主要方法:
- 实现非均的初始激素分布,以加快初始搜索.
- 利用 ε-贪策略来平衡勘探和开采.
- 引入了指数α和β的自适应动态调整.
- 开发了一个考虑距离和转角度的多目标启发式函数.
- 设计了一个动态的全球激素更新策略,以避免局部最佳.
- 将路径规划转化为一个多目标优化问题.
主要成果:
- 模拟证实了IEACO内部每个增强的有效性.
- 与其他路径规划算法相比,IEACO表现出卓越的性能.
- 实验结果验证了IEACO在移动机器人的实际应用.
结论:
- 拟议的IEACO算法显著改善了移动机器人的全球路径规划.
- IEACO克服了标准ACO的局限性,提供了更全面的路径优化.
- 增强的算法在现实应用中显示了实用价值和卓越的性能.
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