A hybrid approach to global optimization using a clustering algorithm in a genetic search framework

作     者：Hanagandi, V Nikolaou, M 

作者机构：GE Ctr Corp Res & Dev Schenectady NY 12301 USA Texas A&M Univ Dept Chem Engn College Stn TX 77843 USA 

出 版 物：《COMPUTERS & CHEMICAL ENGINEERING》 (计算机与化工)

年 卷 期：1998年第22卷第12期

页      面：1913-1925页

核心收录：

学科分类：0817[工学-化学工程与技术] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：hybrid approach global optimization clustering algorithm 

摘      要：The concern of this work is global optimization using genetic algorithms (GAs). In this work we propose a synergy between the cluster analysis technique, popular in classical stochastic global optimization, and the GA to accomplish global optimization. This synergy minimizes redundant searches around local optima and enhances the capability of the GA to explore new areas in the search space. The proposed methodology demonstrates superior performance when compared with the simple GA on benchmark cases. We also report our solution of the optimal pumps configuration synthesis problem. (C) 1998 Elsevier Science Ltd. All rights reserved.