A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
A mixed-integer simulation-based optimization approach with surrogate functions in water resources management
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.
A mixed-integer simulation-based optimization approach with surrogate functions in water resources management
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.
A mixed-integer simulation-based optimization approach with surrogate functions in water resources management
Hemker, Thomas (author) / Fowler, Kathleen R. (author) / Farthing, Matthew W. (author) / Stryk, Oskar (author)
Optimization and Engineering ; 9 ; 341-360
2008-05-20
20 pages
Article (Journal)
Electronic Resource
English
Mixed-integer nonlinear optimization , Computational engineering , Simulation-based optimization , Groundwater management , Surrogate optimization Mathematics , Optimization , Engineering, general , Systems Theory, Control , Environmental Management , Operation Research/Decision Theory , Financial Engineering
| British Library Conference Proceedings | 2008
MISO: mixed-integer surrogate optimization framework
| Springer Verlag | 2015
MISO: mixed-integer surrogate optimization framework
| Online Contents | 2015
Surrogate-Based Optimization for Solving a Mixed Integer Network Design Problem
| British Library Online Contents | 2015