.png){kind=icon}
返回
A novel hybrid differential evolution algorithm with modified CoDE and JADE
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2016.06.011.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
JADE and CoDE are two well-known state-of-the-art DE algorithms for solving global optimization problems (GOPs). JADE is found to be suitable for solving unimodal and simple multimodal functions as an exploitation mutation strategy, i.e.DE/current-to-pbest/1, is employed, while CoDE is shown to fit for handling complicated multimodal functions due to its exploration mutation strategies, such as DE/rand/1 /bin, DE/currant-to-rand/1, and DE/rand/2/bin. To combine their merits for tackling different types of GOPs, a novel hybrid framework is designed based on the modified JADE (MJADE) and modified CoDE (MCoDE), named HMJCDE. Different from the simple combination of MJADE and MCoDE, they are operated alternatively according to the improvement rate of the fitness value. To assess the performance of HMJCDE, 30 benchmark problems taken from CEC2014 competition on real parameter optimization are employed. When compared with JADE, CoDE, other state-of-the-art DE variants and non-DE heuristic algorithms, HMJCDE performs better than all the competitors on most of test problems. Moreover, the sensitivity analysis of some parameters in HMJCDE is conducted and the effectiveness of our proposed hybrid framework is also justified experimentally. (C) 2016 Elsevier B.V. All rights reserved.
Keyword:
Differential evolution
Modified JADE
Modified CoDE
Hybrid framework
Global numerical optimization
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:
6.6
论文数:
1.4W
被引数:
4.8W
机构
引用论文
.png){kind=icon}
Friction modeling and compensation for haptic master manipulator based on deep Gaussian process
PREAI
分享收藏
查看更多内容