請用此 Handle URI 來引用此文件: http://hdl.handle.net/11455/5651
標題: 免疫演算法於配水管網管理問題之研究
A Study of Immune Algorithm on Solving Water Distribution Network Optimization Problem
作者: 朱健瑋
Chu, Chien-Wei
關鍵字: Water Distribution Network
配水管網
Optimization
Immune Algorithm
最佳化
免疫演算法
出版社: 環境工程學系所
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摘要: 隨著生命科學的快速發展,吸引了各領域學者的關注,因而陸續發展出各種模仿生物演化、覓食及防禦方式的演算法。一般最著名的生物型演算法為遺傳演算法,依據達爾文的進化理論進行演算搜尋。但近年許多學者發現遺傳演算法會出現收斂速度緩慢,或過早搜尋至區域最佳解無法跳脫等問題。加上近年來生物免疫學的研究發現,免疫系統具有強大的搜尋能力,於是開始發展與人體免疫系統相關理論的免疫演算法。免疫演算法已被證明具有許多特點,如搜尋能力強、避免過早陷入區域最佳解無法跳脫以及收斂速度快等特點。免疫演算法亦是屬於進化式演算法的一種,可用於各種最佳化問題的演算,目前已成功的應用在許多研究領域上包含了電機排程、運輸物流、資訊安全等。但是對於現有的免疫演算法仍有改良提升的空間,其應用研究則有待推廣到更多的新問題或新領域。因此,本研究將應用免疫演算法求解環境工程領域中之配水管網最佳化問題,其中包括了管網最佳設計以及加氯排程最佳化問題,並將免疫演算法結合遺傳演算法進行強化,組成遺傳免疫演算法,以最佳的效能來求解問題。透過求解紐約設計案例及康乃迪克州實際加氯排程案例的測試檢驗,其結果顯示,免疫演算法及遺傳免疫演算法對於配水管網最佳化問題的求解,無論是品質或是效能皆能達到與文獻相符合的結果甚至具有更佳的求解品質及效率。
Immune algorithm (IA) is a set of computational systems inspired by the defense process of the biological immune system. This study proposed an optimization procedure based on IA framework to optimize the least-cost and scheduling of booster disinfection in water distribution systems. A Genetic Immune Algorithm (GIA) procedure, which employs genetic algorithm (GA) to briefly screen initial antibody repertoires for IA, is also developed. The well-known benchmark instances, New York City Tunnel (NYCT) problem and South Central Connecticut Regional Water Authority (SCCRWA) problem, are utilized as case studies to evaluate the optimization performance of IA and GIA. The least-cost designs of NYCT obtained by IA and GIA are compared with those by GA and fast messy GA (fmGA) previously published in the literature. The results of comparison reveal that IA and GIA are able to find the optimal solutions of NYCT with higher computational efficiency (less number of evaluations) than GA and fmGA. For the optimal scheduling of booster disinfection in SCCRWA, this study has found an optimal booster disinfection injection rate at both the source and disinfection boosters situated at known locations in the distribution network. This case study is then adopted to validate the effectiveness and efficiency of the proposed IA and GIA. The evaluation results confirm the potential of IA and GIA in solving the scheduling of booster disinfection optimization problems. Notable performance enhancement is observed in GIA, indicating that the combination of GA can significantly improve the optimization performance of IA
URI: http://hdl.handle.net/11455/5651
其他識別: U0005-1305200910224900
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