Stochastic discrete optimization
SIAM Journal on Control and Optimization
A review of simulation optimization techniques
Proceedings of the 30th conference on Winter simulation
Stochastic Comparison Algorithm for Discrete Optimization with Estimation
SIAM Journal on Optimization
Simulation Budget Allocation for Further Enhancing theEfficiency of Ordinal Optimization
Discrete Event Dynamic Systems
A Template for Scatter Search and Path Relinking
AE '97 Selected Papers from the Third European Conference on Artificial Evolution
Nested Partitions Method for Global Optimization
Operations Research
New Two-Stage and Sequential Procedures for Selecting the Best Simulated System
Operations Research
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning
Comparisons with a Standard in Simulation Experiments
Management Science
Using Ranking and Selection to "Clean Up" after Simulation Optimization
Operations Research
Simulation optimization: simulation optimization
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Simulation optimization: a review, new developments, and applications
WSC '05 Proceedings of the 37th conference on Winter simulation
Stochastic optimization using model reference adaptive search
WSC '05 Proceedings of the 37th conference on Winter simulation
Discrete Optimization via Simulation Using COMPASS
Operations Research
Allocation of simulation runs for simulation optimization
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
A Model Reference Adaptive Search Method for Global Optimization
Operations Research
Selection of the best with stochastic constraints
Winter Simulation Conference
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Simulation Optimization (SO) is a class of mathematical optimization techniques in which the objective function could only be numerically evaluated through simulation. In this paper, a new SO approach called Golden Region (GR) search is developed for continuous problems. GR divides the feasible region into a number of (sub) regions and selects one region in each iteration for further search based on the quality and distribution of simulated points in the feasible region and the result of scanning the response surface through a metamodel. The experiments show the GR method is efficient compared to three well-established approaches in the literature. We also prove the convergence in probability to global optimum for a large class of random search methods in general and GR in particular.