一个多战略改进的斑马优化算法用于AGV路径规划
Cunji Zhang1,2, Chuangeng Chen1,2, Jiaqi Lu1
1School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004, China.
Biomimetics (Basel, Switzerland)
|October 28, 2025
概括
多策略改进的斑马优化算法 (MIZOA) 通过改进融合和探索来增强群体智能. 这种新的算法在自动引导车辆 (AGV) 的优化和路径规划方面表现出卓越的性能.
科学领域:
- 计算智能是一种计算智能.
- 群体情报算法 群体情报算法
- 超启发式优化优化
背景情况:
- 斑马优化算法 (ZOA) 存在缓慢的融合和局部最佳问题.
- 现有的群集智能算法经常与勘探-开发平衡作斗争.
研究的目的:
- 引入一个改进的斑马优化算法 (ZOA),命名为多策略改进的斑马优化算法 (MIZOA).
- 加强全球收,勘探和开发能力的ZOA.
主要方法:
- 实施了多种人口的搜索策略,以增加多样性.
- 综合基因算法 (GA) 突变与大都会适应性勘探/开发的标准.
- 集成的Coati优化算法 (COA) 狩猎行为和莱维飞行用于增强的全球搜索.
主要成果:
- 在23个基准函数上,MIZOA表现出卓越的全球收准确性和优化性能.
- 统计测试 (Wilcoxon,Friedman) 证实了MIZOA对其他八种算法的稳定性.
- 在真实世界的工程问题和AGV路径规划上,MIZOA的表现优于七个算法.
结论:
- 拟议的MIZOA有效地解决了传统ZOA的局限性.
- MIZOA提供了强大的和有效的优化,特别是在复杂的任务,如AGV路径规划.
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