Pattern search ranking and selection algorithms for mixed variable simulation-based optimization

Todd A. Sriver, James W. Chrissis and Mark A. Abramson

European Journal of Operational Research, 2009, vol. 198, issue 3, 878-890

Abstract: The class of generalized pattern search (GPS) algorithms for mixed variable optimization is extended to problems with stochastic objective functions. Because random noise in the objective function makes it more difficult to compare trial points and ascertain which points are truly better than others, replications are needed to generate sufficient statistical power to draw conclusions. Rather than comparing pairs of points, the approach taken here augments pattern search with a ranking and selection (R&S) procedure, which allows for comparing many function values simultaneously. Asymptotic convergence for the algorithm is established, numerical issues are discussed, and performance of the algorithm is studied on a set of test problems.

Keywords: Pattern; search; algorithms; Mixed; variable; programming; Nonlinear; programming; Simulation; Ranking; and; selection (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:198:y:2009:i:3:p:878-890

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