A HYBRID OPTIMIZATION METHOD FOR WIND GENERATOR DESIGN

被引:0
|
作者
Gao, Xiao-Zhi [1 ]
Wang, Xiaolei [1 ]
Jokinen, Tapani [2 ]
Ovaska, Seppo Jari [2 ]
Arkkio, Antero [2 ]
Zenger, Kai [1 ]
机构
[1] Aalto Univ, Sch Elect Engn, Dept Automat & Syst Technol, FI-00076 Aalto, Finland
[2] Aalto Univ, Sch Elect Engn, Dept Elect Engn, FI-00076 Aalto, Finland
基金
芬兰科学院;
关键词
Harmony search (HS); Cultural algorithm (CA); Search knowledge; Hybrid optimization methods; Wind generator optimization; HARMONY SEARCH ALGORITHM; PARTICLE SWARM OPTIMIZATION; ENGINEERING OPTIMIZATION; DIFFERENTIAL EVOLUTION;
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
The Harmony Search (HS) method is an emerging meta-heuristic optimization algorithm. However, like most of the evolutionary computation techniques, the HS does not store or utilize the useful knowledge gained during the search procedure in an efficient way. In this paper, we propose and study a hybrid optimization approach, in which the HS is merged together with the Cultural Algorithm (CA). Our modified HS method, namely HS-CA, has the interesting feature of embedded problem-solving knowledge. Simulations of some typical benchmark problems demonstrate that the HS-CA can yield a superior optimization performance over the regular HS algorithm. We also apply the proposed HS-CA in a case study of the optimal wind generator design to further examine its effectiveness.
引用
收藏
页码:4347 / 4373
页数:27
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