Combined pattern search and ranking and selection for simulation optimization

A new algorithm class is presented for optimization of stochastic simulation models. The algorithms, which combine generalized pattern search (GPS) with ranking and selection (R&S), require "black-box" simulation evaluations and are applicable to problems with mixed variables (continuo...

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Bibliographic Details
Main Authors: Sriver, Todd A., Chrissis, James W.
Format: Conference Proceeding
Language:English
Subjects:
Applied sciences
Computer science
control theory
systems
Exact sciences and technology
Simulation
Software
Theory of computation
Theory of computation > Design and analysis of algorithms
Theory of computation > Design and analysis of algorithms > Mathematical optimization
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Summary:A new algorithm class is presented for optimization of stochastic simulation models. The algorithms, which combine generalized pattern search (GPS) with ranking and selection (R&S), require "black-box" simulation evaluations and are applicable to problems with mixed variables (continuous, discrete numeric, and categorical). Implementation of the Mixed-variable Generalized Pattern Search with Ranking and Selection (MGPS-RS) algorithm with three different R&S procedures is demonstrated and tested on a small set of standard test functions. Results of this preliminary performance evaluation are summarized and compared with existing search methods.
DOI:10.5555/1161734.1161852