A differential evolution with simulated annealing updating method

被引：0
|
作者：
Yan, Jing-Yu [1 ]
Ling, Qing [1 ]
Sun, De-Min [1 ]
机构：
[1] Univ Sci & Technol China, Automat Dept, Hefei 230027, Peoples R China
来源：
PROCEEDINGS OF 2006 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2006年
关键词：
differential evolution; simulated annealing; function optimization; Global search;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, we point out that conventional differential evolution (CDE) algorithm runs the risk of being trapped by local optima because of its greedy updating strategy and intrinsic differential property. A novel simulated annealing differential evolution (SADE) algorithm is proposed to improve the premature property of CDE. With the aid of simulated annealing updating strategy, BADE is able to escape from the local optima, and achieve the balance between exploration and exploitation. Optimization results on standard test suits indicate that SADE outperforms CDE in the global search ability.
引用
收藏
页码：2103 / +
页数：2
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