基于动态分级策略的改进正余弦算法
TP301; 针对正余弦算法存在易陷入局部最优、求解精度不高、收敛速度较慢等问题,提出一种基于动态分级策略的改进正余弦算法.首先,引入拉丁超立方抽样法,将搜索空间均匀划分,使初始种群覆盖整个搜索空间,以保持初始种群的多样性.其次,采用动态分级策略,根据适应度值的排序情况,将种群动态划分为好中差3个等级,并应用破坏策略与精英引导方法对其进行扰动,以提高算法的收敛精度,增强跳出局部最优的能力.最后,引入反向学习方法,设计了动态反向学习全局搜索策略,以提高算法的收敛速度,同时对改进算法在复杂度、收敛性和稳定性方面进行性能测试,选取15个标准测试函数在低维和高维状态下进行仿真实验分析,并与粒子群算法、...
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| Published in | 系统工程与电子技术 Vol. 43; no. 6; pp. 1596 - 1605 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
西安理工大学机械与精密仪器工程学院,陕西西安710048
01.06.2021
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1001-506X |
| DOI | 10.12305/j.issn.1001-506X.2021.06.17 |
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| Abstract | TP301; 针对正余弦算法存在易陷入局部最优、求解精度不高、收敛速度较慢等问题,提出一种基于动态分级策略的改进正余弦算法.首先,引入拉丁超立方抽样法,将搜索空间均匀划分,使初始种群覆盖整个搜索空间,以保持初始种群的多样性.其次,采用动态分级策略,根据适应度值的排序情况,将种群动态划分为好中差3个等级,并应用破坏策略与精英引导方法对其进行扰动,以提高算法的收敛精度,增强跳出局部最优的能力.最后,引入反向学习方法,设计了动态反向学习全局搜索策略,以提高算法的收敛速度,同时对改进算法在复杂度、收敛性和稳定性方面进行性能测试,选取15个标准测试函数在低维和高维状态下进行仿真实验分析,并与粒子群算法、回溯搜索算法和其他改进正余弦算法进行比较.仿真分析结果表明,所提算法有效地提高了算法的收敛性和稳定性. |
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| AbstractList | TP301; 针对正余弦算法存在易陷入局部最优、求解精度不高、收敛速度较慢等问题,提出一种基于动态分级策略的改进正余弦算法.首先,引入拉丁超立方抽样法,将搜索空间均匀划分,使初始种群覆盖整个搜索空间,以保持初始种群的多样性.其次,采用动态分级策略,根据适应度值的排序情况,将种群动态划分为好中差3个等级,并应用破坏策略与精英引导方法对其进行扰动,以提高算法的收敛精度,增强跳出局部最优的能力.最后,引入反向学习方法,设计了动态反向学习全局搜索策略,以提高算法的收敛速度,同时对改进算法在复杂度、收敛性和稳定性方面进行性能测试,选取15个标准测试函数在低维和高维状态下进行仿真实验分析,并与粒子群算法、回溯搜索算法和其他改进正余弦算法进行比较.仿真分析结果表明,所提算法有效地提高了算法的收敛性和稳定性. |
| Author | 张洋洋 黎俊宇 史云鹏 魏锋涛 |
| AuthorAffiliation | 西安理工大学机械与精密仪器工程学院,陕西西安710048 |
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| Author_FL | ZHANG Yangyang LI Junyu WEI Fengtao SHI Yunpeng |
| Author_FL_xml | – sequence: 1 fullname: WEI Fengtao – sequence: 2 fullname: ZHANG Yangyang – sequence: 3 fullname: LI Junyu – sequence: 4 fullname: SHI Yunpeng |
| Author_xml | – sequence: 1 fullname: 魏锋涛 – sequence: 2 fullname: 张洋洋 – sequence: 3 fullname: 黎俊宇 – sequence: 4 fullname: 史云鹏 |
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| Keywords | 改进正余弦算法 动态分级策略 数值仿真分析 全局搜索策略 拉丁超立方种群初始化策略 |
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| Title | 基于动态分级策略的改进正余弦算法 |
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