基于多维环境中的混合群智能算法的移动平台路径规划方法的研究
Shuai Wang1, Yifan Zhu2, Yuhong Du3,4
1School of Mechanical and Automotive Engineering, Liaocheng University, Liaocheng 252000, China.
Biomimetics (Basel, Switzerland)
|August 27, 2025
概括
一个改进的人工蜂群-甲虫天线搜索 (IABCBAS) 算法通过结合混乱理论和反向学习来增强路径规划. 这种新的方法在复杂的环境中大大缩短了路径距离和规划时间.
科学领域:
- 机器人和人工智能
- 计算智能
- 优化算法
背景情况:
- 像Dijkstra和APF这样的传统路径规划算法需要完整的环境数据,导致复杂性和效率降低.
- 群集智能算法为路径规划提供了强大的数据处理,但存在过早的融合和局部最佳问题.
- 现有的群集智能方法需要改进以提高复杂环境中的多样性和可搜索性.
研究的目的:
- 建议改进人工蜂群-甲虫天线搜索 (IABCBAS) 算法,以实现高效可靠的路径规划.
- 解决传统和现有群集智能算法的路径规划方面的局限性.
- 在路径规划算法中增强人口多样性,空间可搜索性和逃离局部最佳的能力.
主要方法:
- 在人工蜂群算法中引入了帐混乱和非统一的变化,以提高种群多样性和可搜索性.
- 将随机反向学习和贪策略纳入虫天线搜索算法以增强方向查找和局部最佳逃生.
- 实现了适应性权重调整,以平衡两个集成算法之间的全球搜索和本地精细化.
主要成果:
- IABCBAS算法在各种维度和环境复杂度中表现出卓越的路径点搜索性能和高稳定性.
- 除实验证实,引入的优化策略显著提高了路径规划中的收精度和速度.
- 与其他算法相比,IABCBAS在2D环境中减少了23.83%的平均路径规划距离,在3D环境中减少了27.97%的规划时间.
结论:
- 改进的IABCBAS算法与传统和现有的群集智能方法相比,为路径规划提供了更高的效率和可靠性.
- 混沌理论,反向学习和自适应策略的整合有效地克服了个别算法的局限性.
- 该算法的性能改进表明在路径规划中具有实用工程应用的巨大潜力.
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