Chaotic immune differential evolution algorithm

被引:1
作者
Guo Zhenyu [1 ]
Bai Zhifeng [1 ]
Cao Binggang [1 ]
机构
[1] Xian Jiaotong Univ, Sch Mech Engn, Xian 710049, Shaan Xi, Peoples R China
来源
2007 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS, VOLS 1-5 | 2007年
关键词
chaotic; immune; differential evolution; weighting factor; crossover factor;
D O I
10.1109/ROBIO.2007.4522519
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A novel chaotic immune differential evolution algorithm (CEDE) is presented. In CEDE, weighted difference is added to the best individual. Using randomness and space ergodicity of chaotic mapping, the best individual is processed by chaotic immune clone operation; In each iteration process, the weighting factor is changed dynamically based on the current aggregation degree and the number of stopping generations; the crossover factor is changed dynamically based on the current evolution speed. Introduce keeping diversity operation, the problem on premature convergence has been solved. DE and CEDE are tested with three well-known benchmark functions. The numerical experiments indicate that the convergence speed of CEDE is considerably superior to DE, has high efficiency and convergence accuracy.
引用
收藏
页码:2244 / 2249
页数:6
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