A Novel Differential Evolution Algorithm for Constrained Optimization

被引：2
|
作者：
Zhang Yan [1 ]
Bin Zhang [2 ]
Liu Zhaobin [1 ]
机构：
[1] Suzhou Vocat Univ, Suzhou 215104, Peoples R China
[2] Jiaxing Univ, Jiaxing 434023, Zhejiang, Peoples R China
来源：
2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND ENGINEERING (CSE) AND IEEE/IFIP INTERNATIONAL CONFERENCE ON EMBEDDED AND UBIQUITOUS COMPUTING (EUC), VOL 1 | 2017年
基金：
中国国家自然科学基金;
关键词：
self-adaptive differential evolution algorithm(JDE); differential evolution(DE); simple penalty function method(SPFM); constrained optimization problems (COPs);
D O I：
10.1109/CSE-EUC.2017.68
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
A novel differential evolution algorithm is proposed for constrained optimization problems (COPs). The proposed algorithm combines the ideas between the self-adaptive differential evolution algorithm (JDE) and simple penalty function method (SPFM). Simulation results on the bump problem show that the solutions of the new algorithm is better than those of the algorithms in the almost exiting literature and the convergence speed of the new algorithm is faster than that of DE.
引用
收藏
页码：342 / 348
页数：7
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