A Global Archive Sub-Population Genetic Algorithm with Adaptive Strategy in Multi-objective Parallel-Machine Scheduling Problem
學年 94
學期 1
出版（發表）日期 2006-01-01
作品名稱 A Global Archive Sub-Population Genetic Algorithm with Adaptive Strategy in Multi-objective Parallel-Machine Scheduling Problem
作品名稱（其他語言）
著者 Pei-Chann Chang; Shih-Hsin Chen; Jih-Chang Hsieh
單位
出版者
著錄名稱、卷期、頁數 Lecture Notes in Computer Science 4221, p.730-739
摘要 This research extends the sub-population genetic algorithm and combines it with a global archive and an adaptive strategy to solve the multi-objective parallel scheduling problems. In this approach, the global archive is applied within each subpopulation and once a better Pareto solution is identified, other subpopulations are able to employ this Pareto solution to further guide the searching direction. In addition, the crossover and mutation rates are continuously adapted according to the performance of the current generation. As a result, the convergence and diversity of the evolutionary processes can be maintained in a very efficient manner. Intensive experimental results indicate that the sub-population genetic algorithm combing the global archive and the adaptive strategy outperforms NSGA II and SPEA II approaches.
關鍵字
語言 en
ISSN 0302-9743
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 DEU
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/987654321/121469 )