Simulation Budget Allocation for Further Enhancing theEfficiency of Ordinal Optimization
Discrete Event Dynamic Systems
Ordinal Comparison via the Nested Partitions Method
Discrete Event Dynamic Systems
Simulation optimization using balanced explorative and exploitative search
WSC '04 Proceedings of the 36th 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
Simulation optimization with countably infinite feasible regions: Efficiency and convergence
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Proceedings of the 38th conference on Winter simulation
Relative Frequencies of Generalized Simulated Annealing
Mathematics of Operations Research
Discrete Optimization via Simulation Using COMPASS
Operations Research
A framework for locally convergent random-search algorithms for discrete optimization via simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Multi-objective ordinal optimization for simulation optimization problems
Automatica (Journal of IFAC)
Balanced Explorative and Exploitative Search with Estimation for Simulation Optimization
INFORMS Journal on Computing
Industrial strength COMPASS: A comprehensive algorithm and software for optimization via simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
A brief introduction to optimization via simulation
Winter Simulation Conference
Simulation optimization with hybrid golden region search
Winter Simulation Conference
An Adaptive Hyperbox Algorithm for High-Dimensional Discrete Optimization via Simulation Problems
INFORMS Journal on Computing
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Adaptive heuristic search algorithm for discrete variables based multi-objective optimization
Structural and Multidisciplinary Optimization
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In this paper we study a class of discrete optimization problems, where the objective function for a given configuration can be expressed as the expectation of a random variable. In such problems, only samples of the random variables are available for the optimization process. An iterative algorithm called the stochastic comparison (SC) algorithm is developed. The convergence of the SC algorithm is established based on an examination of the quasi-stationary probabilities of a time-inhomogeneous Markov chain. We also present some numerical experiments.