基于学生心理学的增强教育优化算法,用于全球优化问题和真实问题
Wenyu Miao1, Katherine Lin Shu2, Xiao Yang3
1School of Humanities and Social Sciences, Xi'an Jiaotong University, Xi'an 710000, China.
Biomimetics (Basel, Switzerland)
|January 27, 2026
概括
基于学生心理学的增强优化 (ESPBO) 通过解决局部最佳情况和增强趋同来改善无人机轨迹规划. 这种新的算法在复杂的环境中提供了卓越的路径精度和稳定性.
科学领域:
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
- 优化算法 优化算法
背景情况:
- 基于学生心理学的标准优化 (SPBO) 受到有限的探索,局部最佳和差准确性的影响.
- 三维无人机 (UAV) 轨迹规划在复杂的地形和禁飞区中提出了挑战.
研究的目的:
- 增强SPBO算法,以提高3D无人机轨迹规划中的探索,利用和融合精度.
- 引入一个增强的SPBO (ESPBO) 算法,并采用新的战略来实现强大的优化.
主要方法:
- ESPBO集成了时间适应调度,导师池指导和方向跳跃探索.
- 性能与GWO,WOA,IAGWO,DBO,SO,RIME和CEC基准函数上的原始SPBO进行评估.
- 统计稳定性使用威尔科克森等级和弗里德曼测试进行评估.
主要成果:
- 在路径规划准确性和趋同稳定性方面,ESPBO显著优于比较算法,达到第一名.
- 在3D无人机轨迹规划中,ESPBO实现了最佳路径长度为199.8874米,平均长度为205.8179米.
- ESPBO的平均路径长度低于其他测试算法的最佳路径长度.
结论:
- 在复杂的环境中,ESPBO为无人机轨迹优化提供了一种高效,强大的解决方案.
- 改进的算法在现有的集群智能方法上显示了显著的改进.
- ESPBO扩展了群集智能在自主导航系统中的适用性.
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