Two Phase Sub-Population Genetic Algorithm for Parallel Machine Scheduling problem
學年 94
學期 1
出版（發表）日期 2005-10-01
作品名稱 Two Phase Sub-Population Genetic Algorithm for Parallel Machine Scheduling problem
作品名稱（其他語言）
著者 Pei-Chann Chang; Shih-Hsin Chen; Kun-Lin Lin
單位
出版者
著錄名稱、卷期、頁數 Expert Systems with Applications 29(3), p.705-712
摘要 This paper introduces a two‐phase sub population genetic algorithm to solve the parallel machine-scheduling problem. In the first phase, the population will be decomposed into many sub-populations and each sub-population is designed for a scalar multi-objective. Sub-population is a new approach for solving multi-objective problems by fixing each sub-population for a pre-determined criterion. In the second phase, non-dominant solutions will be combined after the first phase and all sub-population will be unified as one big population. Not only the algorithm merges sub-populations but the external memory of Pareto solution is also merged and updated. Then, one unified population with each chromosome search for a specific weighted objective during the next evolution process. The two phase sub-population genetic algorithm is applied to solve the parallel machine-scheduling problems in testing of the efficiency and efficacy. Experimental results are reported and the superiority of this approach is discussed.
關鍵字 Scheduling problem;Genetic algorithm;Multi-objective optimization;Evolution strategy
語言 en
ISSN 0957-4174; 1873-6793
期刊性質 國外
收錄於 SCI
產學合作
通訊作者
審稿制度 是
國別 GBR
公開徵稿
出版型式 ,電子版,紙本
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/121463 )