An improved particle swarm algorithm based on dynamic segmentation and neighborhood reverse learning(基于动态分级和邻域反向学习的改进粒子群算法)
针对粒子群算法容易陷入局部最优解的问题，提出了一种基于动态分级和邻域反向学习的改进粒子群算法.该算法通过构建动态分级机制，将种群中的粒子动态地划分成3个等级，对不同等级内的粒子采取不同的扰动行为，使得粒子在增强种群多样性的同时保持向全局最优方向进化；采用粒子智能更新方式，提高了粒子的搜索能力；引入动态邻域反向学习点建立全局搜索策略，促使种群快速寻优.最后，利用多种典型测试函数对该算法进行仿真实验，结果表明，与其他几种优化算法相比，本算法具有较好的收敛性和稳定性....
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| Format: | Article |
| Language: | zho |
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Zhejiang University Press
2018-05-01
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| Series: | Zhejiang Daxue xuebao. Lixue ban |
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| Online Access: | https://doi.org/10.3785/j.issn.1008-9497.2018.03.001 |
| _version_ | 1797235730084265984 |
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| author | RENYanzhi(任燕芝) |
| author_facet | RENYanzhi(任燕芝) |
| author_sort | RENYanzhi(任燕芝) |
| collection | DOAJ |
| description | 针对粒子群算法容易陷入局部最优解的问题，提出了一种基于动态分级和邻域反向学习的改进粒子群算法.该算法通过构建动态分级机制，将种群中的粒子动态地划分成3个等级，对不同等级内的粒子采取不同的扰动行为，使得粒子在增强种群多样性的同时保持向全局最优方向进化；采用粒子智能更新方式，提高了粒子的搜索能力；引入动态邻域反向学习点建立全局搜索策略，促使种群快速寻优.最后，利用多种典型测试函数对该算法进行仿真实验，结果表明，与其他几种优化算法相比，本算法具有较好的收敛性和稳定性. |
| first_indexed | 2024-04-24T16:52:36Z |
| format | Article |
| id | doaj.art-cca143bb8100447b8bf3a216f691c802 |
| institution | Directory Open Access Journal |
| issn | 1008-9497 |
| language | zho |
| last_indexed | 2024-04-24T16:52:36Z |
| publishDate | 2018-05-01 |
| publisher | Zhejiang University Press |
| record_format | Article |
| series | Zhejiang Daxue xuebao. Lixue ban |
| spelling | doaj.art-cca143bb8100447b8bf3a216f691c8022024-03-29T01:58:38ZzhoZhejiang University PressZhejiang Daxue xuebao. Lixue ban1008-94972018-05-0145326127110.3785/j.issn.1008-9497.2018.03.001An improved particle swarm algorithm based on dynamic segmentation and neighborhood reverse learning(基于动态分级和邻域反向学习的改进粒子群算法)RENYanzhi(任燕芝)0https://orcid.org/0000-0003-1109-8050School of Mathematics and Statistics, Xidian University, Xi 'an 710126, China(西安电子科技大学数学与统计学院，陕西 西安 710126)针对粒子群算法容易陷入局部最优解的问题，提出了一种基于动态分级和邻域反向学习的改进粒子群算法.该算法通过构建动态分级机制，将种群中的粒子动态地划分成3个等级，对不同等级内的粒子采取不同的扰动行为，使得粒子在增强种群多样性的同时保持向全局最优方向进化；采用粒子智能更新方式，提高了粒子的搜索能力；引入动态邻域反向学习点建立全局搜索策略，促使种群快速寻优.最后，利用多种典型测试函数对该算法进行仿真实验，结果表明，与其他几种优化算法相比，本算法具有较好的收敛性和稳定性.https://doi.org/10.3785/j.issn.1008-9497.2018.03.001粒子群算法动态分级机制邻域反向学习全局搜索策略 |
| spellingShingle | RENYanzhi(任燕芝) An improved particle swarm algorithm based on dynamic segmentation and neighborhood reverse learning(基于动态分级和邻域反向学习的改进粒子群算法) Zhejiang Daxue xuebao. Lixue ban 粒子群算法 动态分级机制 邻域反向学习 全局搜索策略 |
| title | An improved particle swarm algorithm based on dynamic segmentation and neighborhood reverse learning(基于动态分级和邻域反向学习的改进粒子群算法) |
| title_full | An improved particle swarm algorithm based on dynamic segmentation and neighborhood reverse learning(基于动态分级和邻域反向学习的改进粒子群算法) |
| title_fullStr | An improved particle swarm algorithm based on dynamic segmentation and neighborhood reverse learning(基于动态分级和邻域反向学习的改进粒子群算法) |
| title_full_unstemmed | An improved particle swarm algorithm based on dynamic segmentation and neighborhood reverse learning(基于动态分级和邻域反向学习的改进粒子群算法) |
| title_short | An improved particle swarm algorithm based on dynamic segmentation and neighborhood reverse learning(基于动态分级和邻域反向学习的改进粒子群算法) |
| title_sort | improved particle swarm algorithm based on dynamic segmentation and neighborhood reverse learning 基于动态分级和邻域反向学习的改进粒子群算法 |
| topic | 粒子群算法 动态分级机制 邻域反向学习 全局搜索策略 |
| url | https://doi.org/10.3785/j.issn.1008-9497.2018.03.001 |
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