Differential evolution algorithm with local abstract convex region partition

被引：0
作者：
Zhou, Xiao-Gen [1 ]
Zhang, Gui-Jun [1 ]
Hao, Xiao-Hu [1 ]
机构：
[1] College of Information Engineering, Zhejiang University of Technology, Hangzhou
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2015年 / 41卷 / 07期
基金：
中国国家自然科学基金;
关键词：
Abstract convex; Differential evolution; Global optimization; Region partition; Underestimate;
D O I：
10.16383/j.aas.2015.c140680
中图分类号：
学科分类号：
摘要：
Within the framework of differential evolution algorithm, a differential evolution algorithm with local abstract convex region partition is proposed in this paper incorporating the abstract convexity theory. Firstly, the partition of the search domain is performed dynamically by building piecewise linear abstract convex lower supporting hyperplanes for the neighboring individuals of new individuals. Secondly, the search domain narrows gradually by using properties of the region partition. Meanwhile, the process of population updating is guided according to the information of underestimate, and poor individuals are identified effectively. Additionally, the generalized descent directions of the lower supporting hyperpalens are used for local enhancement, and the search domain is partitioned again according to the evolutionary information. Finally, some poor individuals get enhanced according to descent directions of their local neighbourhood. Numerical experiment results have verified the effectiveness of the proposed algorithm. ©, 2015, Acta Automatica Sinica. All right reserved.
引用
收藏
页码：1315 / 1327
页数：12
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