Pattern Search Methods for Use-Provided Points
ICCS '01 Proceedings of the International Conference on Computational Science-Part II
Sourcebook of parallel computing
Combined pattern search and ranking and selection for simulation optimization
WSC '04 Proceedings of the 36th conference on Winter simulation
A mathematical framework to optimize ATR systems with non-declarations and sensor fusion
Computers and Operations Research
Nonsmooth optimization through Mesh Adaptive Direct Search and Variable Neighborhood Search
Journal of Global Optimization
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ICS'08 Proceedings of the 12th WSEAS international conference on Systems
Algorithm 909: NOMAD: Nonlinear Optimization with the MADS Algorithm
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Derivative-free methods for bound constrained mixed-integer optimization
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Computers and Operations Research
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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 is not available, and the notion of local optimality must be defined through a user-specified set of neighboring points. We present a class of direct search algorithms to provide limit points that satisfy some appropriate necessary conditions for local optimality for such problems. We give a more expensive version of the algorithm that guarantees additional necessary optimality conditions. A small example illustrates the differences between the two versions. A real thermal insulation system design problem illustrates the efficacy of the user controls for this class of algorithms.