Pattern Search Algorithms for Mixed Variable Programming
نویسندگان
چکیده
منابع مشابه
Pattern Search Algorithms for Mixed Variable Programming
Many engineering optimization problems involve a special kind of discrete variable that can be represented by a number, but this representation has no significance. Such variables arise when a decision involves some situation like a choice from an unordered list of categories. This has two implications: The standard approach of solving problems with continuous relaxations of discrete variables ...
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A new class of algorithms for solving nonlinearly constrained mixed variable optimization problems is presented. This class combines and extends the Audet-Dennis Generalized Pattern Search (GPS) algorithms for bound constrained mixed variable optimization, and their GPS-filter algorithms for general nonlinear constraints. In generalizing existing algorithms, new theoretical convergence results ...
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The class of generalized pattern search (GPS) algorithms for mixed variable optimization is extended to problems with stochastic objective functions, by augmenting it with ranking and selection (R&S). Asymptotic convergence for the algorithm is established, numerical issues are discussed, and performance of the algorithm is studied on a set of test problems.
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In previous work, the generalized pattern search (GPS) algorithm for linearly constrained (continuous) optimization was extended to mixed variable problems, in which some of the variables may be categorical. In another paper, the mesh adaptive direct search (MADS) algorithm was introduced as a generalization of GPS for problems with general nonlinear constrained. The convergence analyses of the...
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ژورنال
عنوان ژورنال: SIAM Journal on Optimization
سال: 2001
ISSN: 1052-6234,1095-7189
DOI: 10.1137/s1052623499352024