Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/5204
標題: 啟發式演算法在地下水復育優選問題之應用
The Application of Heuristic Algorithms on the Optimization of Groundwater Remediation Problems
作者: 李姵穎
Lee, Pei-Yin
關鍵字: nonlinear programming
非線性規劃
tabu search
simulated annealing
genetic algorithms
hybrid method
optimization
groundwater remediation
禁忌搜尋法
模擬退火法
遺傳演算法
混合優選模式
地下水復育優選問題
出版社: 環境工程學系所
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摘要: 在地下水復育優選問題中,經常須處理涵蓋多維度的決策變數且具高度非線性的函數,因此經常造成求解上的困難,傳統上求解此類的問題多以非線性規劃(nonlinear programming, NLP)來求解,但在傳統的方法中容易使搜尋陷入局部解,以致於無法獲得全域最佳解。 因此,近年來發展出所謂的啟發式(heuristic)優選技術如禁忌搜尋法(tabu search, TS)、模擬退火法(simulated annealing, SA)、遺傳演算法(genetic algorithms, GA)、螞蟻演算法(ant algorithms)等,強調具有跳脫局部解並能接受劣化解之特性,本研究將利用TS、SA、GA以及傳統的NLP來求解地下水復育優選問題;但由於每種演算法的尋優能力常受限於本身的特性,且各有其優缺點,因此再以GA為前處理器,分別與NLP、TS、SA結合為混合(hybrid)模式並求解地下水復育優選問題。 本研究以虛擬、拘限(confined)、均質(homogeneous)及非均質(heterogeneous)之地下水復育案例,並分別以NLP、TS、SA、GA以及混合優選模式GA-NLP、GA-TS及GA-SA針對該案例予以求解,以了解各優選技術之求解能力及效率。 研究結果顯示,各案例中,每種優選模式都有其發揮之處,NLP和GA-NLP在求解各類問題時,求解效率都相當快,而GA-TS對於求解最小抽取量的求解品質能優於其他演算法,GA則在求解能力上都能展現出穩定的狀態,此外,研究結果也發現混合優選模式對於解的品質和求解能力有所幫助。
The Optimization of groundwater remediation problems often needs to handle multivariate decision and the function of greatly non-linearity, causing difficulties in solution. The conventional method to solve these kinds of problems is nonlinear programming (NLP); however, the search of such traditional method tends to fall into local optimum instead of global optima. Therefore, heuristic and advanced techniques such as tabu search (TS), simulated annealing (SA), genetic algorithms (GA), and ant algorithms have been developed in recent years to enhance the global optimization ability. This study employs TS, SA, GA, and the conventional NLP to find out the solution of optimization of groundwater remediation problems. Furthermore, due to the searching ability of every algorithm is often circumscribed by its own attribute and each have its own merits and defects, we use GA as pre-processor, combining NLP, TS, and SA respectively as hybrid models (GA-NLP, GA-TS, and GA-SA) to solve optimization of groundwater remediation problems. Two hypothetical, confined aquifers with homogeneous and heterogeneous hydraulic conductivities are used as case studies. The results show that NLP and GA-NLP converged more quickly to the solutions than other methods; GA-TS obtained the best quality solutions of the minimum pumping rate problems, while GA displayed the most stable and reliable performance. In addition, the results also reveal that hybrid model is able to improve the solution quality and reliability of the groundwater optimization problems.
URI: http://hdl.handle.net/11455/5204
其他識別: U0005-2408200615305600
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-2408200615305600
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