Accelerating the convergence of random search methods for discrete stochastic optimization
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Global random optimization by simultaneous perturbation stochastic approximation
Proceedings of the 33nd conference on Winter simulation
A Novel Sampling Approach to Combinatorial Optimization Under Uncertainty
Computational Optimization and Applications
Variable-sample methods for stochastic optimization
ACM Transactions on Modeling and Computer Simulation (TOMACS)
A combined procedure for optimization via simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Selecting the best stochastic system for large scale problems in DEDS
Mathematics and Computers in Simulation
Optimization via simulation: a combined procedure for optimization via simulation
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Computers and Operations Research
Approximate Implementations of Pure Random Search in the Presence of Noise
Journal of Global Optimization
Solution quality of random search methods for discrete stochastic optimization
Mathematics and Computers in Simulation
Simulation-based optimization using simulated annealing with confidence interval
WSC '04 Proceedings of the 36th conference on Winter simulation
Simulation optimization using balanced explorative and exploitative search
WSC '04 Proceedings of the 36th conference on Winter simulation
A new method to determine the tool count of a semiconductor factory using FabSim
WSC '04 Proceedings of the 36th conference on Winter simulation
A distributed computing architecture for simulation and optimization
WSC '05 Proceedings of the 37th conference on Winter simulation
Two simulated annealing algorithms for noisy objective functions
WSC '05 Proceedings of the 37th conference on Winter simulation
Discrete optimization via simulation using coordinate search
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
Simulation optimization with countably infinite feasible regions: Efficiency and convergence
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Discrete Optimization via Simulation Using COMPASS
Operations Research
An analytically derived cooling schedule for simulated annealing
Journal of Global Optimization
A framework for locally convergent random-search algorithms for discrete optimization via simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Multi-objective simulation optimization through search heuristics and relational database analysis
Decision Support Systems
Simulated annealing in the presence of noise
Journal of Heuristics
Simulation-based optimization for the quay crane scheduling problem
Proceedings of the 40th Conference on Winter Simulation
Balanced Explorative and Exploitative Search with Estimation for Simulation Optimization
INFORMS Journal on Computing
A survey on metaheuristics for stochastic combinatorial optimization
Natural Computing: an international journal
Industrial strength COMPASS: A comprehensive algorithm and software for optimization via simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
A multiobjective metaheuristic for a mean-risk multistage capacity investment problem
Journal of Heuristics
Efficient selectivity and backup operators in Monte-Carlo tree search
CG'06 Proceedings of the 5th international conference on Computers and games
A multiobjective metaheuristic for a mean-risk static stochastic knapsack problem
Computational Optimization and Applications
A brief introduction to optimization via simulation
Winter Simulation Conference
On the performance of the cross-entropy method
Winter Simulation Conference
Mathematical and Computer Modelling: An International Journal
Adaptive search with stochastic acceptance probabilities for global optimization
Operations Research Letters
ACM Transactions on Modeling and Computer Simulation (TOMACS)
A VNS algorithm for noisy problems and its application to project portfolio analysis
SAGA'07 Proceedings of the 4th international conference on Stochastic Algorithms: foundations and applications
Efficient discrete optimization via simulation using stochastic kriging
Proceedings of the Winter Simulation Conference
Adaptive probabilistic branch and bound for level set approximation
Proceedings of the Winter Simulation Conference
An Adaptive Hyperbox Algorithm for High-Dimensional Discrete Optimization via Simulation Problems
INFORMS Journal on Computing
Adaptive heuristic search algorithm for discrete variables based multi-objective optimization
Structural and Multidisciplinary Optimization
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We present a modification of the simulated annealing algorithm designed for solving discrete stochastic optimization problems. Like the original simulated annealing algorithm, our method has the hill climbing feature, so it can find global optimal solutions to discrete stochastic optimization problems with many local solutions. However, our method differs from the original simulated annealing algorithm in that it uses a constant (rather than decreasing) temperature. We consider two approaches for estimating the optimal solution. The first approach uses the number of visits the algorithm makes to the different states (divided by a normalizer) to estimate the optimal solution. The second approach uses the state that has the best average estimated objective function value as estimate of the optimal solution. We show that both variants of our method are guaranteed to converge almost surely to the set of global optimal solutions, and discuss how our work applies in the discrete deterministic optimization setting. We also show how both variants can be applied for solving discrete optimization problems when the objective function values are estimated using either transient or steady-state simulation. Finally, we include some encouraging numerical results documenting the behavior of the two variants of our algorithm when applied for solving two versions of a particular discrete stochastic optimization problem, and compare their performance with that of other variants of the simulated annealing algorithm designed for solving discrete stochastic optimization problems.