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53
Filter Pattern Search Algorithms for Mixed Variable Constrained Optimization Problems
 SIAM Journal on Optimization
, 2004
"... A new class of algorithms for solving nonlinearly constrained mixed variable optimization problems is presented. This class combines and extends the AudetDennis Generalized Pattern Search (GPS) algorithms for bound constrained mixed variable optimization, and their GPSfilter algorithms for gene ..."
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Cited by 55 (6 self)
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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 AudetDennis Generalized Pattern Search (GPS) algorithms for bound constrained mixed variable optimization, and their GPSfilter algorithms for general nonlinear constraints. In generalizing existing algorithms, new theoretical convergence results are presented that reduce seamlessly to existing results for more specific classes of problems. While no local continuity or smoothness assumptions are required to apply the algorithm, a hierarchy of theoretical convergence results based on the Clarke calculus is given, in which local smoothness dictate what can be proved about certain limit points generated by the algorithm. To demonstrate the usefulness of the algorithm, the algorithm is applied to the design of a loadbearing thermal insulation system. We believe this is the first algorithm with provable convergence results to directly target this class of problems.
Simulated Annealing Algorithms For Continuous Global Optimization
, 2000
"... INTRODUCTION In this paper we consider Simulated Annealing algorithms (SA in what follows) applied to continuous global optimization problems, i.e. problems with the following form f = min x2X f(x); (1.1) where X ` ! n is a continuous domain, often assumed to be compact, which, combined with ..."
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Cited by 47 (1 self)
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INTRODUCTION In this paper we consider Simulated Annealing algorithms (SA in what follows) applied to continuous global optimization problems, i.e. problems with the following form f = min x2X f(x); (1.1) where X ` ! n is a continuous domain, often assumed to be compact, which, combined with the continuity or lower semicontinuity of f , guarantees the existence of the minimum value f . SA algorithms are based on an analogy with a physical phenomenon: while at high temperatures the molecules in a liquid move freely, if the temperature is slowly decreased the thermal mobility of the molecules is lost and they form a pure crystal which also corresponds to a state of minimum energy. If the temperature is decreased too quickly (the so called quenching) a liquid metal rather ends up in a polycrystalline or amorphous state with
Tuning Search Algorithms for RealWorld Applications: A Regression Tree Based Approach
, 2004
"... The optimization of complex realworld problems might benefit from well tuned algorithm's parameters. We propose a methodology that performs this tuning in an effective and efficient algorithmical manner. This approach combines methods from statistical design of experiments, regression analysis ..."
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Cited by 34 (5 self)
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The optimization of complex realworld problems might benefit from well tuned algorithm's parameters. We propose a methodology that performs this tuning in an effective and efficient algorithmical manner. This approach combines methods from statistical design of experiments, regression analysis, design and analysis of computer experiments methods, and treebased regression. It can also be applied to analyze the influence of different operators or to compare the performance of different algorithms. An evolution strategy and a simulated annealing algorithm that optimize an elevator supervisory group controller system are used to demonstrate the applicability of our approach to realworld optimization problems.
Bayesian Hierarchical Modeling for Integrating LowAccuracy and HighAccuracy Experiments
 Technometrics
, 2008
"... Standard practice in analyzing data from different types of experiments is to treat data from each type separately. By borrowing strength across multiple sources, an integrated analysis can produce better results. Careful adjustments need to be made to incorporate the systematic differences among va ..."
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Cited by 31 (3 self)
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Standard practice in analyzing data from different types of experiments is to treat data from each type separately. By borrowing strength across multiple sources, an integrated analysis can produce better results. Careful adjustments need to be made to incorporate the systematic differences among various experiments. To this end, some Bayesian hierarchical Gaussian process models (BHGP) are proposed. The heterogeneity among different sources is accounted for by performing flexible location and scale adjustments. The approach tends to produce prediction closer to that from the highaccuracy experiment. The Bayesian computations are aided by the use of Markov chain Monte Carlo and Sample Average Approximation algorithms. The proposed method is illustrated with two examples: one with detailed and approximate finite elements simulations for mechanical material design and the other with physical and computer experiments for modeling a food processor.
Massively Parallel Simulated Annealing and its Relation to Evolutionary Algorithms
 EVOLUTIONARY COMPUTATION
, 1994
"... Simulated annealing and and single trial versions of evolution strategies possess a close relationship when they are designed for optimization over continuous variables. Analytical investigations of their differences and similarities lead to a crossfertilization of both approaches, resulting in new ..."
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Cited by 23 (2 self)
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Simulated annealing and and single trial versions of evolution strategies possess a close relationship when they are designed for optimization over continuous variables. Analytical investigations of their differences and similarities lead to a crossfertilization of both approaches, resulting in new theoretical results, new parallel population based algorithms, and a better understanding of the interrelationships.
Global Optimization For Constrained Nonlinear Programming
, 2001
"... In this thesis, we develop constrained simulated annealing (CSA), a global optimization algorithm that asymptotically converges to constrained global minima (CGM dn ) with probability one, for solving discrete constrained nonlinear programming problems (NLPs). The algorithm is based on the necessary ..."
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Cited by 14 (2 self)
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In this thesis, we develop constrained simulated annealing (CSA), a global optimization algorithm that asymptotically converges to constrained global minima (CGM dn ) with probability one, for solving discrete constrained nonlinear programming problems (NLPs). The algorithm is based on the necessary and sufficient condition for constrained local minima (CLM dn ) in the theory of discrete constrained optimization using Lagrange multipliers developed in our group. The theory proves the equivalence between the set of discrete saddle points and the set of CLM dn, leading to the firstorder necessary and sufficient condition for CLM dn. To find
SPOT: An R Package For Automatic and Interactive Tuning of Optimization Algorithms by Sequential Parameter Optimization. arXiv.org ePrint archive,
, 2010
"... Abstract The sequential parameter optimization (spot) package for R (R Development Core Team, 2008) is a toolbox for tuning and understanding simulation and optimization algorithms. Modelbased investigations are common approaches in simulation and optimization. Sequential parameter optimization ha ..."
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Cited by 13 (8 self)
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Abstract The sequential parameter optimization (spot) package for R (R Development Core Team, 2008) is a toolbox for tuning and understanding simulation and optimization algorithms. Modelbased investigations are common approaches in simulation and optimization. Sequential parameter optimization has been developed, because there is a strong need for sound statistical analysis of simulation and optimization algorithms. spot includes methods for tuning based on classical regression and analysis of variance techniques; treebased models such as CART and random forest; Gaussian process models (Kriging) and combinations of different metamodeling approaches. This article exemplifies how spot can be used for automatic and interactive tuning.
Using Combinatorial Optimization Methods for Quantification Scheduling
"... Model checking is the process of verifying whether a model o a coK452wG t system satisfies a specified tempomp property. Symbolic algoP90wG basedo n Binary Decisio Diagrams (BDDs) have significantly increased the sizeo the mo dels that can be verified. The mainprow42 in symbo licmo del checking is t ..."
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Cited by 11 (1 self)
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Model checking is the process of verifying whether a model o a coK452wG t system satisfies a specified tempomp property. Symbolic algoP90wG basedo n Binary Decisio Diagrams (BDDs) have significantly increased the sizeo the mo dels that can be verified. The mainprow42 in symbo licmo del checking is the image computVN7B problem, i.e., e#ciently co4j97Kw the successoK o r predecesso5 o f a seto f states. This paper is an indepth studyo the imagecoew5O7j5w pro4Kj4 We analyze and evaluate several newheuristics, metrics, and algo979wG fo thisprow0P0 The algoj25wG use co binato0wG oto0wG4Pj2 techniques such as hill climbing,simulat d annealing,andordering by recursive partWBBVN3F to oO0 better results than was previo4wG the case. Theo70wG42 analysis and systematic experimentatio are used to evaluate the algoPKwG47
Nested spacefilling designs for computer experiments with two levels of accuracy.
 Statist. Sinica
, 2009
"... Abstract: Computer experiments with different levels of accuracy have become prevalent in many engineering and scientific applications. Design construction for such computer experiments is a new issue because the existing methods deal almost exclusively with computer experiments with one level of a ..."
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Cited by 11 (5 self)
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Abstract: Computer experiments with different levels of accuracy have become prevalent in many engineering and scientific applications. Design construction for such computer experiments is a new issue because the existing methods deal almost exclusively with computer experiments with one level of accuracy. In this paper, we construct some nested spacefilling designs for computer experiments with two levels of accuracy. Our construction makes use of Galois fields and orthogonal arrays.