This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Andradóttir, S. Articles by Prudius, A. A. Search for Related Content

Balanced Explorative and Exploitative Search with Estimation for Simulation Optimization

H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332
Quantitative Financial Development, Bloomberg LP, New York, New York 10022
sa{at}gatech.edu
aprudius{at}gmail.com

We discuss desirable features that optimization algorithms should possess to exhibit good empirical performance when applied to solve simulation optimization problems possessing little known structure. Our framework emphasizes maintaining an appropriate balance between exploration, exploitation, and estimation. With the exception of estimation, our ideas are also applicable in (unstructured) deterministic optimization. Exploration refers to (globally) searching the entire feasible region for promising solutions, exploitation refers to the (local) search for improved solutions in promising subregions, and estimation refers to obtaining enhanced estimates of the objective function values at promising solutions and of the optimal solution. We also present two new random search methods that possess these desirable features, prove their almost-sure global convergence, and provide preliminary numerical results that suggest that the proposed framework is promising from a practical point of view.

Key words: stochastic optimization; random search; sampling
History: received September 2006; revised August 2008; accepted September 2008.