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CMRLCCOA:用于工程设计和高超音速车辆路径规划的多策略增强型Coati优化算法
Gang Hu1,2, Haonan Zhang1, Ni Xie1
1Department of Applied Mathematics, Xi'an University of Technology, Xi'an 710054, China.
Biomimetics (Basel, Switzerland)
|July 26, 2024
概括
增强的coati优化算法 (CMRLCCOA) 通过整合混乱映射,反向学习,Lévy飞行和交叉策略来提高搜索速度和精度. 这提高了优化性能,避免了局部优化.
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:
- 科蒂优化算法 (COA) 面临着缓慢的搜索速度和有限的优化精度的挑战.
- 现有的元启发算法经常因过早的融合和对搜索空间的不足探索而扎.
研究的目的:
- 引入一个增强的Coati优化算法 (CMRLCCOA),解决原来的COA的局限性.
- 为了提高Coati优化算法的搜索速度,优化精度和稳定性.
主要方法:
- 使用Sine混乱映射初始化,以提高人口质量和多样性.
- 凸透镜成像反向学习策略,扩大候选解决方案的搜索范围.
- 莱维的飞行策略是增加搜索步骤大小,防止过早的收.
- 交叉策略减少搜索盲点,引导粒子向全球最佳方向.
主要成果:
- 与现有算法相比,CMRLCCOA在CEC2017和CEC2019基准数据集上表现出卓越的表现.
- 代的收曲线,框图和统计分析证实了收准确度的提高和避免局部最佳值.
- 对高超音速车辆巡航轨道优化的应用比比较方法的路径长度更短.
结论:
- 拟议的CMRLCCOA有效地提高了Coati优化算法的效率,精度和稳定性.
- CMRLCCOA显示出解决包括工程应用在内的复杂优化问题的巨大潜力.
- 多种策略的整合提供了一个强大而有竞争力的优化框架.
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