Adaptive Differential Evolution Algorithm based on Gradient and Polar Coordinates Search Strategies

被引：1
|
作者：
Yang, Jun [1 ]
Wei, Jingxuan [1 ]
Liu, Jiang [1 ]
机构：
[1] Xidian Univ, Sch Comp Sci & Technol, Xian 710071, Shaanxi, Peoples R China
来源：
2015 11TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY (CIS) | 2015年
关键词：
differential evolution algorithm; gradient descent; polar coordinates; GLOBAL OPTIMIZATION;
D O I：
10.1109/CIS.2015.74
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Adaptive differential evolution algorithm based on gradient and polar coordinates search strategies (ADE) is proposed in this paper. In order to improve the precision of solutions, gradient and polar coordinates search strategies are introduced. Since the gradient search strategy generates offsprings using the derivative definition, it will accelerate the convergence speed. Polar coordinates search strategy can help the algorithm jump out of the local optimization and avoid continuously searching in wrong direction. The simulation results show that the proposed algorithm has better results compare to SaDE, NSDE and CMAES for benchmark functions 1 similar to 14 in CEC2005.
引用
收藏
页码：274 / 277
页数：4
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