A modified particle swarm optimization algorithm based on dynamic learning factors and sharing method(基于动态因子和共享适应度的改进粒子群算法)
为提高粒子群算法的收敛速度和优化性能，避免陷入局部最优，提出了一种基于动态学习因子和共享适应度函数的改进粒子群算法.在惯性权重w随着迭代次数非线性减少而动态调整学习因子的基础上，引入共享适应度函数.当算法未达到终止条件而收敛时，利用粒子和最优解间距离挑选一批粒子重新初始化形成新群体，并用共享适应度函数对新群体进行评价，新旧2个群体分别追随自己的局部最优解直至迭代结束.对4个典型多峰复杂函数的测试结果表明，该改进算法不仅加快了寻得最优解的速度，而且提高了粒子群算法全局收敛的性能....
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| Format: | Article |
| Language: | zho |
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Zhejiang University Press
2016-11-01
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| Series: | Zhejiang Daxue xuebao. Lixue ban |
| Subjects: | |
| Online Access: | https://doi.org/10.3785/j.issn.1008-9497.2016.06.014 |
| _version_ | 1797235780166352896 |
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| author | TANYifeng(谭熠峰) SUNTingting(孙婷婷) XUXinming(徐新民) |
| author_facet | TANYifeng(谭熠峰) SUNTingting(孙婷婷) XUXinming(徐新民) |
| author_sort | TANYifeng(谭熠峰) |
| collection | DOAJ |
| description | 为提高粒子群算法的收敛速度和优化性能，避免陷入局部最优，提出了一种基于动态学习因子和共享适应度函数的改进粒子群算法.在惯性权重w随着迭代次数非线性减少而动态调整学习因子的基础上，引入共享适应度函数.当算法未达到终止条件而收敛时，利用粒子和最优解间距离挑选一批粒子重新初始化形成新群体，并用共享适应度函数对新群体进行评价，新旧2个群体分别追随自己的局部最优解直至迭代结束.对4个典型多峰复杂函数的测试结果表明，该改进算法不仅加快了寻得最优解的速度，而且提高了粒子群算法全局收敛的性能. |
| first_indexed | 2024-04-24T16:53:24Z |
| format | Article |
| id | doaj.art-362d80ad809f4f7ba41aada92cb40b3b |
| institution | Directory Open Access Journal |
| issn | 1008-9497 |
| language | zho |
| last_indexed | 2024-04-24T16:53:24Z |
| publishDate | 2016-11-01 |
| publisher | Zhejiang University Press |
| record_format | Article |
| series | Zhejiang Daxue xuebao. Lixue ban |
| spelling | doaj.art-362d80ad809f4f7ba41aada92cb40b3b2024-03-29T01:58:36ZzhoZhejiang University PressZhejiang Daxue xuebao. Lixue ban1008-94972016-11-0143669670010.3785/j.issn.1008-9497.2016.06.014A modified particle swarm optimization algorithm based on dynamic learning factors and sharing method(基于动态因子和共享适应度的改进粒子群算法)TANYifeng(谭熠峰)0https://orcid.org/0000-0003-1151-9206SUNTingting(孙婷婷)1XUXinming(徐新民)2https://orcid.org/0000-0002-0910-2375College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou 310027, China(浙江大学信息与电子工程学院，浙江 杭州 310027)College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou 310027, China(浙江大学信息与电子工程学院，浙江 杭州 310027)College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou 310027, China(浙江大学信息与电子工程学院，浙江 杭州 310027)为提高粒子群算法的收敛速度和优化性能，避免陷入局部最优，提出了一种基于动态学习因子和共享适应度函数的改进粒子群算法.在惯性权重w随着迭代次数非线性减少而动态调整学习因子的基础上，引入共享适应度函数.当算法未达到终止条件而收敛时，利用粒子和最优解间距离挑选一批粒子重新初始化形成新群体，并用共享适应度函数对新群体进行评价，新旧2个群体分别追随自己的局部最优解直至迭代结束.对4个典型多峰复杂函数的测试结果表明，该改进算法不仅加快了寻得最优解的速度，而且提高了粒子群算法全局收敛的性能.https://doi.org/10.3785/j.issn.1008-9497.2016.06.014动态学习因子共享适应度粒子群算法 |
| spellingShingle | TANYifeng(谭熠峰) SUNTingting(孙婷婷) XUXinming(徐新民) A modified particle swarm optimization algorithm based on dynamic learning factors and sharing method(基于动态因子和共享适应度的改进粒子群算法) Zhejiang Daxue xuebao. Lixue ban 动态 学习因子 共享适应度 粒子群算法 |
| title | A modified particle swarm optimization algorithm based on dynamic learning factors and sharing method(基于动态因子和共享适应度的改进粒子群算法) |
| title_full | A modified particle swarm optimization algorithm based on dynamic learning factors and sharing method(基于动态因子和共享适应度的改进粒子群算法) |
| title_fullStr | A modified particle swarm optimization algorithm based on dynamic learning factors and sharing method(基于动态因子和共享适应度的改进粒子群算法) |
| title_full_unstemmed | A modified particle swarm optimization algorithm based on dynamic learning factors and sharing method(基于动态因子和共享适应度的改进粒子群算法) |
| title_short | A modified particle swarm optimization algorithm based on dynamic learning factors and sharing method(基于动态因子和共享适应度的改进粒子群算法) |
| title_sort | modified particle swarm optimization algorithm based on dynamic learning factors and sharing method 基于动态因子和共享适应度的改进粒子群算法 |
| topic | 动态 学习因子 共享适应度 粒子群算法 |
| url | https://doi.org/10.3785/j.issn.1008-9497.2016.06.014 |
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