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基于自适应性coati优化算法的3D无人机的路径规划和工程问题
Chuan Jia1, Ling He2, Dan Liu1
1Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang, 550025, Guizhou, China.
适应式Coati优化算法 (ACOA) 通过提高勘探,融合速度和准确性来增强原来的COA. 在优化任务和现实世界的工程挑战中,ACOA表现出卓越的性能.
科学领域:
- 计算智能是一种计算智能.
- 超启发式优化优化
- 算法设计 算法设计
背景情况:
- 科蒂优化算法 (COA) 面临着不平衡的勘探-开发,缓慢的融合和局部最佳陷等挑战.
- 解决这些局限性对于提高元启发式优化算法的效率和有效性至关重要.
研究的目的:
- 引入一个增强的变体,即适应式Coati优化算法 (ACOA),旨在克服COA的局限性.
- 为了提高勘探-开采平衡,融合速度和优化问题的准确性.
主要方法:
- ACOA集成了混乱映射,以增强随机性和全球搜索.
- 使用随机质子的动态对抗式学习方法减轻了过早的融合.
- 适应性征收飞行策略保持了人口多样性,以防止局部最佳捕获.
- 基于等号干扰的差异进化策略消除了表现不佳的个体.
主要成果:
- 阿科亚在勘探和开采之间取得了卓越的平衡.
- 关于CEC-2017和CEC-2022基准的实验结果显示,ACOA的表现优于COA和其他最先进的算法.
- 威尔科克森的等级总和测试证实了ACOA在统计学上显著的性能改善.
- 在现实世界的工程挑战和无人机路径规划方面,ACOA证明了其有效性.
结论:
- 拟议的ACOA有效地解决了原来的COA的局限性.
- ACOA提供了增强的稳定性,融合准确性和全球搜索功能.
- ACOA显示了复杂的优化问题和现实世界的应用的巨大潜力.
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