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研究生:陳建富
研究生(外文):Jian-Fu Chen
論文名稱:具權重特性偵測器配置問題之研究
論文名稱(外文):A Study of Weighted Detector Allocation Problem
指導教授:陳大正陳大正引用關係
學位類別:碩士
校院名稱:國立虎尾科技大學
系所名稱:資訊管理研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:中文
論文頁數:62
中文關鍵詞:免疫演算法粒子群最佳化演算法網格技術偵測器配置進化式演算法
外文關鍵詞:Immune AlgorithmParticle Swarm OptimizationGrid ComputingDetector AllocationEvolutionary Algorithm
相關次數:
  • 被引用被引用:1
  • 點閱點閱:148
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  • 下載下載:2
  • 收藏至我的研究室書目清單書目收藏:0
本研究所探討的偵測器配置問題是考量在具備權重特性及成本限制的條件下,於特定平面區域內如何決定不同種類、數目之偵測器的位置,而欲使這些特定區域內具備權重特性的最大偵測失效率達到最小化,讓服務品質在不同區域可以達到服務的一致性。在本研究中探討多類別偵測器的配置問題,針對偵測器之類別、數量、位置及人口權重作為最佳化配置之考量,並應用網格運算為基礎之混合式進化演算法求解,其整合免疫演算法及粒子群最佳化演算法兩種演算法。研究方法是利用免疫演算法決定偵測器的數量與類別之後,再由粒子群最佳化演算法決定偵測器應該配置的最佳位置,所利用之演算法透過網格運算技術提升整體運算速度與效率。由實驗數值呈現本研究所提出之方法與最佳化商用套裝軟體LINGO的求解結果比較,所提方法皆優於或同於LINGO之求解結果,顯示所本研究所使用方法之優越性。
This study investigated the weighted detector allocation problems in which the types of detectors and the corresponding numbers and locations are to be decided simultaneously so as to minimize the maximum detecting failure rate in a specified area. In other words, the objective of the detector allocation problem is to minimize the maximum failure rate by deciding the optimal type of detector, numbers of each detector type and where to build up each of them within a specified plan. The weighted detector allocation problem is based on the number of population in each allocation of the specified area. In which, more population is with higher weight. Through this study, we wish to build up the mathematical model and then provide the best strategy to allocate the detectors optimally. In this study, a grid computing based hybrid meta-evolutionary approach is developed for overcoming the difficulties and finding the optimal solutions for the detector allocation problems efficiently and effectively. Through two numerical experiments, we compared our results against the commercial data mining software and other methods in literature, and then we show experimentally that the proposed approach is promising for improving prediction accuracy and enhancing the modeling simplicity.
中文摘要....i
英文摘要....ii
誌謝....iii
目錄....iv
表目錄....vi
圖目錄....vii
第一章 緒論....1
1.1 研究背景與動機....1
1.2 研究範圍與目的....1
1.3 研究方法與架構....2
第二章 文獻探討....5
2.1 設施區位問題....5
2.2 偵測器配置問題....9
2.3 免疫演算法....10
2.3.1 免疫系統....10
2.3.2 免疫演算法流程與步驟....15
2.4 粒子群最佳化演算法....16
2.4.1 粒子群最佳化介紹....16
2.4.2 粒子群最佳化流程與步驟....22
2.5 網格運算....24
第三章 研究方法....26
3.1 問題描述定義與假設....26
3.1.1 基本假設與符號....26
3.2 結合進化式演算法....29
3.3 網格技術之架構....29
3.3.1 網格運算工作分配....31
3.3.2 網格運算流程步驟....31
3.3.3 基因表示與解碼....34
3.3.4 交換與突變....35
3.3.5 懲罰值函數與目標函數....37
3.3.6 終止條件與輸出結果....37
第四章 實驗數據與結果....38
4.1 軟硬體環境與參數設定....38
4.2 研究樣本與實驗結果....39
4.2.1 數據結果....40
4.2.2 數據座標圖....43
4.2.3 數據比較....47
4.2.4 網格效益....49
4.2.5 微調程序....50
4.3 小結....52
第五章 結論....53
5.1 研究結論....53
5.2 未來展望....53
參考文獻....55
附錄一 測試點座標及權重....58
附錄二 最佳化軟體Lingo程式碼....60
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