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The design of simulation/optimizatio...

Kalwij, Ineke Margot.

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The design of simulation/optimization modeling techniques for nonlinear dynamic groundwater systems.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	The design of simulation/optimization modeling techniques for nonlinear dynamic groundwater systems./
作者:	Kalwij, Ineke Margot.
面頁冊數:	148 p.
附註:	Major Professor: Richard C. Peralta.
Contained By:	Dissertation Abstracts International66-04B.
標題:	Engineering, Agricultural. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3170296
ISBN:	9780542069932

The design of simulation/optimization modeling techniques for nonlinear dynamic groundwater systems.
Kalwij, Ineke Margot.

The design of simulation/optimization modeling techniques for nonlinear dynamic groundwater systems. - 148 p.

Major Professor: Richard C. Peralta.

Thesis (Ph.D.)--Utah State University, 2004.

Three new simulation/optimization (S/O) modeling techniques are developed and tested: (i) the Intelligent Space Tube Optimizer (ISTO); (ii) the Global Converger (GC); and (iii) the Robustness Enhancing Optimizer (REO). The main objective of developing the ISTO and the GC is to improve optimization efficiency for large and complex transport optimization problems, and the main objective of developing the REO is to develop robust optimal pumping strategy designs.

ISBN: 9780542069932Subjects--Topical Terms:

1019504
Engineering, Agricultural.

The design of simulation/optimization modeling techniques for nonlinear dynamic groundwater systems.
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520 $a Three new simulation/optimization (S/O) modeling techniques are developed and tested: (i) the Intelligent Space Tube Optimizer (ISTO); (ii) the Global Converger (GC); and (iii) the Robustness Enhancing Optimizer (REO). The main objective of developing the ISTO and the GC is to improve optimization efficiency for large and complex transport optimization problems, and the main objective of developing the REO is to develop robust optimal pumping strategy designs.
520 $a ISTO includes multiple cycles and for each cycle the multi-dimensional space tube is defined, strategies are generated, and genetic algorithm (GA) optimization is performed and calls trained artificial neural networks (ANNs) as a substitute simulator. A space tube consists of overlapping multi-dimensional subspaces, and lengthens in the direction of the optimal solution. Its radius is automatically adjusted between cycles based on optimization performance. Application to an assumed problem demonstrates that the ISTO generally converges within 0.5% of the globally optimal solution. However, specifying an inappropriate initial space tube radius or too few simulations for ANN training degrades performance. Further, there is a trade-off between computational time and desired degree of convergence. For the Blain Naval Ammunition Depot (NAD), ISTO develops optimal pumping strategies generally more efficiently than a hybrid heuristic optimization approach, which uses GA and tabu seach (TS).
520 $a The GC uses GA and TS, and dynamically adapts TS parameters affecting solution space size, search coarseness, TS probability, and tabu list size, based on optimization performance. Applying the CG to a hypothetical problem shows that the GC yields better results within 1000 simulations than GA-TS and GA alone. Applying the GC to NAD demonstrates that the GC is more efficient than the GA-TS or GA alone for solving a large-scale and complex transport optimization problem (NAD).
520 $a The REO couples GA and TS as optimizers and incorporates aquifer parameter sensitivity analysis to guide multiple-realization optimization. This combined approach maximizes strategy robustness for a pumping strategy that is optimal for the objective function. The REO is applied to develop robust least cost strategies for the Umatilla Chemical Depot model. Results show that the REO efficiently develops robust optimal pumping strategies while maintaining least cost values.
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