对于无人机群路径规划和工程设计问题而言,一个增强的除雪优化器
Jinyi Xie1, Jiacheng He2, Zehua Gao3
1School of Mathematics and Information Science, Guangzhou University, Guangzhou, 510000 China.
Heliyon
|September 24, 2024
概括
增强的雪除尘优化 (ESAO) 算法通过解决缓慢的收和局部优化来改进原始算法. 欧空局在基准测试和UAV路径规划等实际应用中表现出卓越的表现.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 超听证学是一种超听证学.
背景情况:
- 雪除尘优化器 (SAO) 是一种有效的优化算法,但它面临着缓慢的融合和局部优化的挑战.
- 解决这些局限性对于提高元启发算法在复杂问题解决中的性能至关重要.
研究的目的:
- 引入一个增强的雪除尘优化算法 (ESAO),克服原来的SAO的局限性.
- 改进优化算法的全球最佳识别,本地最佳逃脱能力和整体搜索性能.
主要方法:
- 实施了适应性T分布控制策略,以改善探索性位置调整.
- 引入了考奇突变策略,以提高逃离局部极端的能力.
- 开发了一个基于领导者的边界控制策略,以提高搜索性能,准确性,速度和稳定性.
主要成果:
- 在29个CEC2017基准函数中,ESAO与8个流行的算法取得了第一名的排名.
- 该算法在解决无人机群路径规划问题方面表现出卓越的性能.
- 在两个工程设计问题上,ESAO优于竞争对手的算法,显示出高质量和稳定的解决方案.
结论:
- ESAO显著提高了Snow Ablation Optimizer的有效性,提供了更好的准确性,速度和稳定性.
- 拟议的算法显示出强大的实际应用潜力,特别是在复杂的优化任务,如无人机路径规划和工程设计.
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