Friday, 11:00 - 11:25 h, Room: H 3003A

 

Juliane Müller
A surrogate model algorithm for computationally expensive mixed-integer black-box global optimization problems

Coauthors: Robert Piché, Christine Ann Shoemaker

 

Abstract:
We present a surrogate model algorithm for computationally expensive mixed-integer black-box global optimization problems that may have computationally expensive constraints. The goal is to find accurate solutions with relatively few function evaluations. A radial basis function surrogate model is used to select candidates for integer and continuous decision variable points at which the computationally expensive objective and constraint functions are to be evaluated. In every iteration multiple new points are selected based on different methods, and the objective and constraint functions are evaluated in parallel. The algorithm converges to the global optimum almost surely. The performance of this new algorithm (SO-MI) is compared to a branch and bound algorithm for nonlinear problems, a genetic algorithm, and the NOMAD (Nonsmooth Optimization by Mesh Adaptive Direct Search) algorithm for mixed-integer problems on test problems from the literature, and application problems arising from structural optimization. The numerical results show that SO-MI reaches significantly better results than the other algorithms.

 

Talk 2 of the invited session Fri.1.H 3003A
"MINLP and constrained optimization without derivatives" [...]
Cluster 6
"Derivative-free & simulation-based optimization" [...]

 

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