A floating-point genetic algorithm for solving the unit commitment problem

作     者：Dang, Chuangyin Li, Minqiang 

作者机构：City Univ Hong Kong Dept Mfg Engn & Engn Management Kowloon Hong Kong Peoples R China Tianjin Univ Inst Syst Engn Tianjin 300072 Peoples R China 

出 版 物：《EUROPEAN JOURNAL OF OPERATIONAL RESEARCH》 (欧洲运筹学杂志)

年 卷 期：2007年第181卷第3期

页      面：1370-1395页

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 070104[理学-应用数学] 0701[理学-数学] 

基　　金：City University of Hong Kong  CityU 

主　　题：unit commitment floating-point genetic algorithm dynamic genetic strategy electrical power generation generators scheduling and economic dispatch 

摘      要：This paper proposes a floating-point genetic algorithm (FPGA) to solve the unit commitment problem (UCP). Based on the characteristics of typical load demand, a floating-point chromosome representation and an encoding-decoding scheme are designed to reduce the complexities in handling the minimum up/down time limits. Strategic parameters of the FPGA are characterized in detail, i.e., the evaluation function and its constraints, population size, operation styles of selection, crossover operation and probability, mutation operation and probability. A dynamic combination scheme of genetic operators is formulated to explore and exploit the FPGA in the non-convex solution space and multimodal objective function. Experiment results show that the FPGA is a more effective technique among the various styles of genetic algorithms, which can be applied to the practical scheduling tasks in utility power systems. (C)] 2006 Elsevier B.V. All rights reserved.