Asymptotically efficient adaptive control in stochastic regression models
Advances in Applied Mathematics
Stochastic discrete optimization
SIAM Journal on Control and Optimization
A globally convergent stochastic approximation
SIAM Journal on Control and Optimization
Integrating and accelerating tabu search, simulated annealing, and genetic algorithms
Annals of Operations Research - Special issue on Tabu search
A method for discrete stochastic optimization
Management Science
Discrete stochastic optimization via a modification of the stochastic ruler method
WSC '96 Proceedings of the 28th conference on Winter simulation
Accelerating the convergence of the stochastic ruler method for discrete stochastic optimization
Proceedings of the 29th conference on Winter simulation
A combined procedure for optimization via simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Optimization via simulation: a combined procedure for optimization via simulation
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Simulation optimization using balanced explorative and exploitative search
WSC '04 Proceedings of the 36th conference on Winter simulation
Combined pattern search and ranking and selection for simulation optimization
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
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)
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Discrete stochastic approximation algorithms for design of optimal sensor fusion rules
International Journal of Sensor Networks
Balanced Explorative and Exploitative Search with Estimation for Simulation Optimization
INFORMS Journal on Computing
Adaptive parameterized improving hit-and-run for global optimization
Optimization Methods & Software - GLOBAL OPTIMIZATION
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
Winter Simulation Conference
Appointment scheduling using optimisation via simulation
Proceedings of the Winter Simulation Conference
Handling stochastic constraints in discrete optimization via simulation
Proceedings of the Winter Simulation Conference
Hi-index | 0.00 |
We discuss the choice of the estimation of the optimal solution when random search methods are applied to solve discrete stochastic optimization problems. At the present time, such optimization methods usually estimate the optimal solution using either the feasible solution the method is currently exploring or the feasible solution visited most often so far by the method. We propose using all the observed objective function values generated as the random search method moves around the feasible region seeking an optimal solution to obtain increasingly more precise estimates of the objective function values at the different points in the feasible region. At any given time, the feasible solution that has the best estimated objective function value (largest one for maximization problems; the smallest one for minimization problems) is used as the estimate of the optimal solution. We discuss the advantages of using this approach for estimating the optimal solution and present numerical results showing that modifying an existing random search method to use tnhis approach for estimating the optimal soluation appears to yield improved performance. We also present sereval rate of convergence results for random search methods using our approach for estimating the optimal solution. One these random search methods is a new variant of the stochastic comparison method; in addition to specifying the rate of convergence of this method, we prove that it is guaranteed to converge almost surely to the set of global optimal solutions and present a result that demonstrates that this method is likely to perform well in practice.