Modified the Performance of Differential Evolution Algorithm with Dual Evolution Strategy

被引：0
|
作者：
Wu, Ying-Chih [1 ]
Lee, Wei-Ping [1 ]
Chien, Ching-Wei [1 ]
机构：
[1] Chung Yuan Christian Univ, Informat Management Dept, Chun Li 32023, Taiwan
来源：
PROCEEDINGS OF 2009 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND COMPUTING (IACSIT ICMLC 2009) | 2009年
关键词：
Differential Evolution (DE); Particle Swarm Optimization (PSO); Differential Evolution Particle Swarm Optimization (DEPSO); Dual Evolution Strategy (EDS);
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Differential Evolution (DE) is one of the novel algorithms of evolution computation. Although it performs superiorly, DE has several disadvantages. In this study, we proposed the construction of a novel DEPSO algorithm in DE and Particle Swarm Optimization (PSO). DEPSO is a strategy of Dual Evolution (DES) based on the master-apprentice mechanism for sharing information. During the iteration, between the two algorithms can be iterative operation to improve the drawbacks "easy to drop into region optimum" moreover increasing the performance to obtain the advantage of accuracy solving and stable convergence.
引用
收藏
页码：57 / 63
页数：7
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