Lipschitzian optimization without the Lipschitz constant
Journal of Optimization Theory and Applications
An asymptotic allocation for simultaneous simulation experiments
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
New Two-Stage and Sequential Procedures for Selecting the Best Simulated System
Operations Research
Selecting the best system: selecting the best system: theory and methods
Proceedings of the 35th conference on Winter simulation: driving innovation
Global optimization with the direct algorithm
Global optimization with the direct algorithm
A testbed of simulation-optimization problems
Proceedings of the 38th conference on Winter simulation
Adaptation of the UOBYQA algorithm for noisy functions
Proceedings of the 38th conference on Winter simulation
Variable-Number Sample-Path Optimization
Mathematical Programming: Series A and B
A computational study on different penalty functions with DIRECT algorithm
ICCSA'13 Proceedings of the 13th international conference on Computational Science and Its Applications - Volume 1
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DIRECT (DIviding RECTangles) is a deterministic global optimization algorithm for bound-constrained problems. The algorithm, based on a space-partitioning scheme, performs both global exploration and local exploitation. In this paper, we modify the deterministic DIRECT algorithm to handle noisy function optimization. We adopt a simple approach that replicates multiple function evaluations per point and takes an average to reduce functional uncertainty. Particular features of the DIRECT method are modified using acquired Bayesian sample information to determine appropriate numbers of replications. The noisy version of the DIRECT algorithm is suited for simulation-based optimization problems. The algorithm is a sampling approach, that only uses objective function evaluations. We have applied the new algorithm in a number of noisy glo al optimizations, including an ambulance base simulation optimization problem.