.png){kind=icon}
返回
A dynamic clustering based differential evolution algorithm for global optimization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ejor.2006.10.053.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A dynamic clustering based differential evolution algorithm (CDE) for global optimization is proposed to improve the performance of the differential evolution (DE) algorithm. With population evolution, CDE algorithm gradually changes from exploring promising areas at the early stages to exploiting solution with high precision at the later stages. Experiments on 28 benchmark problems, including 13 high dimensional functions, show that the new method is able to find near optimal solutions efficiently. Compared with other existing algorithms, CDE improves solution accuracy with less computational effort. (c) 2006 Elsevier B.V. All rights reserved.
Keyword:
global optimization
continuous optimization
differential evolutionary algorithm
clustering method
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6
论文数:
2.2W
被引数:
6.4W
机构
暂无机构信息
引用论文
.png){kind=icon}
分享收藏
查看更多内容