具權重特性偵測器配置問題之研究

A Study of Weighted Detector Allocation Problem

陳建富(Jian-Fu Chen)

指導教授 ： 陳大正

國立虎尾科技大學/管理學院/資訊管理研究所/碩士(2009年)

https://doi.org/10.6827/NFU.2009.00076

摘要

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。

關鍵字

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

並列摘要

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.

並列關鍵字

Immune Algorithm ； Particle Swarm Optimization ； Grid Computing ； Detector Allocation ； Evolutionary Algorithm

參考文獻

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