淡江大學機構典藏:Item 987654321/121440
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    題名: Sub-Population Genetic Algorithm with Mining Gene Structures for multiobjective FlowShop Scheduling Problems
    作者: Chang, Pei-Chann;Chen, Shih-Hsin;Liu, Chen-Hao
    關鍵詞: Genetic algorithms;Multiobjective optimization;Pareto optimum solution;Minging gene structures;Scheduling problem
    日期: 2007-10
    上傳時間: 2021-10-05 12:10:27 (UTC+8)
    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.
    關聯: Expert Systems with Applications 33(3), p.762-771
    DOI: 10.1016/j.eswa.2006.06.019
    顯示於類別:[資訊工程學系暨研究所] 期刊論文

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