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研究生： 林中儀
Lin, Zhong-Yi
論文名稱： 應用適應性多目標差分演化演算法求解電力調度之成本與污染最佳化問題
A Self-adaptive Multiobjective Differential Evolution Algorithm for the Environmental/Economic Dispatch Problem
指導教授： 蔣宗哲
學位類別： 碩士
Master
系所名稱： 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年： 2018
畢業學年度： 106
語文別： 中文
論文頁數： 48
中文關鍵詞： 多目標最佳化 、電力調度 、差分演化演算法 、汙染
DOI URL： http://doi.org/10.6345/THE.NTNU.DCSIE.004.2018.B02
論文種類： 學術論文
相關次數： 點閱：177  下載：3 
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    • 生活在21世紀的人類生活已經不能沒有電力，而目前的台灣也飽受空氣汙染的影響，電力調度之成本與污染最佳化問題探討的是如何分配機組的發電量以達到用最少成本與最低的汙染氣體排放量來提供所需之電力，在綠能還不穩定且核能無法得到共識的現在，火力發電為主流的國家都會面臨這個問題。
    本研究利用差分演化演算法搭配多目標框架MOEA/D嘗試解決這個問題，在所做的實驗中探討各種參數與策略的效果，試著找出最佳的設定。既有論文在比較其提出方法之優劣時多半未採用多目標演算法領域常用的指標，本研究會利用多目標演算法常用的效能指標來評估好壞並且釋出完整的求解資料以供後面的研究者可以進行比較。

    目 錄 中文摘要 i 致 謝 ii 目 錄 iii 附表目錄 v 附圖目錄 vi 第一章 緒論 1 1.1 研究背景 1 1.2 問題定義 2 1.3 多目標最佳化問題 4 1.4 研究方法與貢獻 5 1.5 論文架構 6 第二章 文獻探討 7 2.1 EED 評估解的品質的三種觀點 7 2.2 差分演化演算法求解 EED 8 2.3 其他演算法求解 EED 9 第三章 方法與步驟 11 3.1 差分演化演算法 11 3.2 多目標框架 MOEA/D 12 3.3 初始化族群 15 3.4 修復個體 15 3.5 分配權重與鄰域 16 3.5.1 產生初始權重並分配至個體 16 3.5.2 分配鄰域 16 3.6 產生子代 17 3.6.1 參數控制 17 3.6.2 Polynomial Mutation 與隨機演化策略 18 3.6.3 演化策略的控制 19 3.7 評估個體與環境選擇 19 3.8 終止條件 20 第四章 實驗數據與效能評比 21 4.1 實驗問題集 21 4.2 效能指標與實驗設定 22 4.3 多目標框架的比較 26 4.4 jDE 參數控制的效用 29 4.5 MOEA/D 的環境選擇方式比較 35 4.6 演化策略的控制 36 4.7 與其他文獻比較兩端點極值 41 第五章 結論與未來展望 45 參考文獻 46

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