A hybrid optimization method of harmony search and opposition-based learning

被引：50

作者：

Gao, X. Z. ^{[1
]}

Wang, X. ^{[1
]}

Ovaska, S. J. ^{[2
]}

Zenger, K. ^{[1
]}

机构：

[1] Aalto Univ, Sch Elect Engn, Dept Automat & Syst Technol, FI-00076 Aalto, Finland

[2] Aalto Univ, Dept Elect Engn, Sch Elect Engn, FI-00076 Aalto, Finland

来源：

ENGINEERING OPTIMIZATION | 2012年 / 44卷 / 08期

基金：

芬兰科学院;

关键词：

harmony search (HS); opposition-based learning (OBL); hybrid optimization methods; nonlinear function optimization; optimal wind generator design; GLOBAL OPTIMIZATION; ALGORITHM; DESIGN;

D O I：

10.1080/0305215X.2011.628387

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The harmony search (HS) method is an emerging meta-heuristic optimization algorithm. However, like most of the evolutionary computation techniques, it sometimes suffers from a rather slow search speed, and fails to find the global optimum in an efficient way. In this article, a hybrid optimization approach is proposed and studied, in which the HS is merged together with the opposition-based learning (OBL). The modified HS, namely HS-OBL, has an improved convergence property. Optimization of 24 typical benchmark functions and an optimal wind generator design case study demonstrate that the HS-OBL can indeed yield a superior optimization performance over the regular HS method.

引用

页码：895 / 914

页数：20

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