Sub-Population Genetic Algorithm with Mining Gene Structures for multiobjective FlowShop Scheduling Problems
學年 96
學期 1
出版（發表）日期 2007-10-01
作品名稱 Sub-Population Genetic Algorithm with Mining Gene Structures for multiobjective FlowShop Scheduling Problems
作品名稱（其他語言）
著者 Pei-Chann Chang; Shih-Hsin Chen; Chen-Hao Liu
單位
出版者
著錄名稱、卷期、頁數 Expert Systems with Applications 33(3), p.762-771
摘要 According to previous research of Chang et al. [Chang, P. C., Chen, S. H., & Lin, K. L. (2005b). Two phase sub-population genetic algorithm for parallel machine scheduling problem. Expert Systems with Applications, 29(3), 705–712], the sub-population genetic algorithm (SPGA) is effective in solving multiobjective scheduling problems. Based on the pioneer efforts, this research proposes a mining gene structure technique integrated with the SPGA. The mining problem of elite chromosomes is formulated as a linear assignment problem and a greedy heuristic using threshold to eliminate redundant information. As a result, artificial chromosomes are created according to this gene mining procedure and these artificial chromosomes will be reintroduced into the evolution process to improve the efficiency and solution quality of the procedure. In addition, to further increase the quality of the artificial chromosome, a dynamic threshold procedure is developed and the flowshop scheduling problems are applied as a benchmark problem for testing the developed algorithm. Extensive tests in the flow-shop scheduling problem show that the proposed approach can improve the performance of SPGA significantly.
關鍵字 Genetic algorithms;Multiobjective optimization;Pareto optimum solution;Minging gene structures;Scheduling problem
語言 en
ISSN 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
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機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/121440 )