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A mixed-integer simulation-based optimization approach with surrogate functions in water resources management

Hemker, Thomas and Fowler, K. R. and Farthing, M. W. and Stryk, Oskar von and Stryk, Oskar von (2008):
A mixed-integer simulation-based optimization approach with surrogate functions in water resources management.
In: Optimization and Engineering, pp. 341-360, 9, (4), DOI: 10.1007/s11081-008-9048-0,
[Article]

Abstract

Efficient and powerful methods are needed to overcome the inherent difficulties in the numerical solution of many simulation-based engineering design problems. Typically, expensive simulation codes are included as black-box function generators; therefore, gradient information that is required by mathematical optimization methods is entirely unavailable. Furthermore, the simulation code may contain iterative or heuristic methods, low-order approximations of tabular data, or other numerical methods which contribute noise to the objective function. This further rules out the application of Newton-type or other gradient-based methods that use traditional finite difference approximations. In addition, if the optimization formulation includes integer variables the complexity grows even further. In this paper we consider three different modeling approaches for a mixed-integer nonlinear optimization problem taken from a set of water resources benchmarking problems. Within this context, we compare the performance of a genetic algorithm, the implicit filtering algorithm, and a branch-and-bound approach that uses sequential surrogate functions. We show that the surrogate approach can greatly improve computational efficiency while locating a comparable, sometimes better, design point than the other approaches.

Item Type: Article
Erschienen: 2008
Creators: Hemker, Thomas and Fowler, K. R. and Farthing, M. W. and Stryk, Oskar von and Stryk, Oskar von
Title: A mixed-integer simulation-based optimization approach with surrogate functions in water resources management
Language: English
Abstract:

Efficient and powerful methods are needed to overcome the inherent difficulties in the numerical solution of many simulation-based engineering design problems. Typically, expensive simulation codes are included as black-box function generators; therefore, gradient information that is required by mathematical optimization methods is entirely unavailable. Furthermore, the simulation code may contain iterative or heuristic methods, low-order approximations of tabular data, or other numerical methods which contribute noise to the objective function. This further rules out the application of Newton-type or other gradient-based methods that use traditional finite difference approximations. In addition, if the optimization formulation includes integer variables the complexity grows even further. In this paper we consider three different modeling approaches for a mixed-integer nonlinear optimization problem taken from a set of water resources benchmarking problems. Within this context, we compare the performance of a genetic algorithm, the implicit filtering algorithm, and a branch-and-bound approach that uses sequential surrogate functions. We show that the surrogate approach can greatly improve computational efficiency while locating a comparable, sometimes better, design point than the other approaches.

Journal or Publication Title: Optimization and Engineering
Volume: 9
Number: 4
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Simulation, Systems Optimization and Robotics Group
Date Deposited: 20 Jun 2016 23:26
DOI: 10.1007/s11081-008-9048-0
Identification Number: 2007:OptEng-HemkerFowlervonStryk
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