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題名 | 建構人造染色體於多目標流程型排程問題之基因演算法=Artificial Chromosomes Embedded in Sub-Population Genetic Algorithm for a Multi-Objective Flowshop Scheduling Problems |
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作者姓名(中文) | 林烱禮; 王彥文; | 書刊名 | 清雲學報 |
卷期 | 31:1 2011.01[民100.01] |
頁次 | 頁83-95 |
分類號 | 494.542 |
關鍵詞 | 流程型排程問題; 多目標排程; 人造染色體; 柏拉圖求解; 子群體基因演算法; Flowshop scheduling problem; Multi-objective scheduling; Artificial chromosome; Pareto solutions; Sub-population Genetic Algorithms; |
語文 | 中文(Chinese) |
中文摘要 | 在多目標最佳化問題中,子群體基因演算法是一種基於母體的啟發式搜尋 方法,不同於單一目標的子群體基因演算法,它是利用在多種目標中找尋柏拉 圖最佳解。由於傳統基因演算法在求解過程中,常因為收斂過快導致陷入局部 最佳解,進而減少搜尋解的空間,因此,此研究中,我們將利用兩種不同的人 造染色體運算元導入在某些世代數中,其注入具有獨特性且求解較佳的染色 體,這樣的機制能提供較大的求解空間獲得較佳的解。實驗結果顯示,在測試 案例中,多目標排程問題透過兩種運算元的處理能快速收斂,且同時達到柏拉 圖求解的均勻分散。 |
英文摘要 | Sub-population Genetic Algorithms is a population-based approach in multiple objectives optimization problems. Different from the single objective problem, sub-population genetic algorithms is used to find the Pareto solutions of different objectives. However, the traditional mechanic in the genetic algorithms will diminish the searching space while evolving; it causes the solutions fall into the local optima. In this research, two different kind of artificial chromosome operators will be introduced. The artificial chromosomes will inject to the population to search a better combination of chromosomes while Genetic Algorithms is evolving, this mechanism will provide a more expansive searching space. The experiments result shows that these two operators possess fast convergence and the average scatter of Pareto solutions simultaneously for solving multi-objective scheduling problems in the test instances. |
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