SIAM Journal on Applied Mathematics
Metropolis-type annealing algorithms for global optimization in Rd
SIAM Journal on Control and Optimization
Efficient optimization through response surface modeling: a grope algorithm
Efficient optimization through response surface modeling: a grope algorithm
Annealing of Iterative Stochastic Schemes
SIAM Journal on Control and Optimization
A survey of ranking, selection, and multiple comparison procedures for discrete-event simulation
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Statistical selection of the best system
Proceedings of the 33nd conference on Winter simulation
Rates of Convergence for a Class of Global Stochastic Optimization Algorithms
SIAM Journal on Optimization
Feature Article: Optimization for simulation: Theory vs. Practice
INFORMS Journal on Computing
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
Geometry construction from caustic images
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
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This paper explores an approach to global, stochastic, simulation optimization which combines stochastic approximation (SA) with simulated annealing (SAN). SA directs a search of the response surface efficiently, using a conservative number of simulation replications to approximate the local gradient of a probabilistic loss function. SAN adds a random component to the SA search, needed to escape local optima and forestall premature termination. Using a limited set of simple test problems, we compare the performance of SA/SAN with the commercial package OptQuest. Results demonstrate that SA/SAN can outperform OptQuest when properly tuned. The practical difficulty lies in specifying an appropriate set of SA/SAN gain coefficients for a given application. Further results demonstrate that a multi-start approach greatly improves the coverage and robustness of SA/SAN, while also providing insights useful in directing iterative improvement of the gain coefficients before each new start. This preliminary study is sufficiently encouraging to invite further research on SA/SAN.