A hybrid PBIL-based harmony search method

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• 作者：X. Z. Gao (1) xiao-zhi.gao@aalto.fi
  X. Wang (1) xiaolei.wang@aalto.fi
  T. Jokinen (2) tapani.jokinen@aalto.fi
  S. J. Ovaska (2) seppo.ovaska@aalto.fi
  A. Arkkio (2) antero.arkkio@aalto.fi
  K. Zenger (1) kai.zenger@aalto.fi
• 关键词：Harmony search (HS) – ; Population ; based incremental learning (PBIL) – ; Hybrid optimization methods – ; Nonlinear function optimization – ; Wind generator optimization
• 刊名：Neural Computing & Applications
• 出版年：2012
• 出版时间：July 2012
• 年：2012
• 卷：21
• 期：5
• 页码：1071-1083
• 全文大小：827.9 KB
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• 作者单位：1. Department of Automation and Systems Technology, Aalto University School of Electrical Engineering, Otaniementie 17, 00076 Aalto, Finland2. Department of Electrical Engineering, Aalto University School of Electrical Engineering, Otakaari 5 A, 00076 Aalto, Finland
• ISSN：1433-3058

文摘

The harmony search (HS) method is a popular meta-heuristic optimization algorithm, which has been extensively employed to handle various engineering problems. However, it sometimes fails to offer a satisfactory convergence performance under certain circumstances. In this paper, we propose and study a hybrid HS approach, HS–PBIL, by merging the HS together with the population-based incremental learning (PBIL). Numerical simulations demonstrate that our HS–PBIL is well capable of outperforming the regular HS method in dealing with nonlinear function optimization and a practical wind generator optimization problem.