A hybrid surrogate and pattern search optimization method and application to microelectronics

Zhang, T.; Choi, Kyung K.; Rahman, Sharif; Cho, K.; Baker, P H; Shakil, M.; Heitkamp, Dale H.

doi:10.1007/s00158-006-0034-x

Cited by 27 publications

(14 citation statements)

References 21 publications

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“…Recently, metaheuristics have been combined with deterministic methods such as pattern search to form hybrids that perform global and local search [2,6,13,25,51,59,61,64,66]. As mentioned in Section 1, the new approach described in this paper generalizes Gray et al [25]; see also closely related work in [26,58].…”

Section: Hops In the Context Of Hybrid Optimizationmentioning

confidence: 62%

“…In [61], a few iterations of DIRECT are followed by a few iterations of generalized pattern search (a special case of GSS). Other examples of integrating GSS into a sequential hybrid algorithm may be found in [51,59,66]. In our strategy, the executions are interleaved, though it is not the case that the iterations of the individual solvers are necessarily interleaved.…”

Section: Order Of Executionmentioning

confidence: 99%

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Asynchronous parallel hybrid optimization combining DIRECT and GSS

Griffin

Kolda

2010

Optimization Methods and Software

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In this paper, we explore hybrid parallel global optimization using Dividing Rectangles (DIRECT) and asynchronous generating set search (GSS). Both DIRECT and GSS are derivative-free and so require only objective function values; this makes these methods applicable to a wide variety of science and engineering problems. DIRECT is a global search method that strategically divides the search space into ever-smaller rectangles, sampling the objective function at the centre point for each rectangle. GSS is a local search method that samples the objective function at trial points around the current best point, i.e. the point with the lowest function value. Latin hypercube sampling can be used to seed GSS with a good starting point. Using a set of global optimization test problems, we compare the parallel performance of DIRECT and GSS with hybrids that combine the two methods. Our experiments suggest that the hybrid methods are much faster than DIRECT and scale better when more processors are added. This improvement in performance is achieved without any sacrifice in the quality of the solution -the hybrid methods find the global optimum whenever DIRECT does.

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Section: Hops In the Context Of Hybrid Optimizationmentioning

confidence: 62%

Section: Order Of Executionmentioning

confidence: 99%

Asynchronous parallel hybrid optimization combining DIRECT and GSS

Griffin

Kolda

2010

Optimization Methods and Software

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“…Also, analytic sensitivity is not widely available in industries where commercial codes are mostly used for modeling and simulation. Instead, sampling-based methods that constructs the surrogate model using a limited number of sample data are very useful (Gu et al, 2001;Simpson et al, 2001;Buranathiti et al, 2004;Queipo et al, 2005;Zhang et al, 2006;Kim and Choi, 2008;Dubourg et al, 2011). Among existing surrogate modeling methods, Kriging method (Sacks et al, 1989) that divides the approximate response into two parts, the mean structure and the stochastic process with zero mean, is a popular one (Jin et al, 2003;Goel et al, 2009).…”

Section: Introductionmentioning

confidence: 98%

Sampling-Based RBDO of Ship Hull Structures Considering Thermo-Elasto-Plastic Residual Deformation

Choi

Cho

Choi

et al. 2014

Mechanics Based Design of Structures and Machines

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We present a shape optimization method using a sampling-based RBDO method linked with a commercial finite element analysis (FEA) code ANSYS, which is applicable to residual deformation problems of the ship hull structure in welding process. The programming language ANSYS Parametric Design Language (APDL) and shell elements are used for the thermo-elasto-plastic analysis. The shape of the ship hull structure is modeled using the bicubic Ferguson patch and coordinate components of vertices, tangential vectors of boundary curves are selected as design variables. The sensitivity of probabilistic constraint is calculated from the probabilistic sensitivity analysis using the score function and Monte Carlo Simulation (MCS) on the surrogate model constructed by using the Dynamic Kriging (DKG) method. The sequential quadratic programming (SQP) algorithm is used for the optimization. In two numerical examples, the suggested optimization method is applied to practical residual deformation problems in welding ship hull structures, which proves the sampling-based RBDO can be successfully utilized for obtaining a reliable optimum design in highly nonlinear multi-physics problem of thermo-elasto-plasticity.

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“…Since then this approach has been popular among optimizers and practitioners (see [1,2,4,8,12,15,19,20,22]). …”

Section: Introductionmentioning

confidence: 99%

A surrogate management framework using rigorous trust-region steps

Gratton¹,

Vicente²

2012

Optimization Methods and Software

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Surrogate models are frequently used in the optimization engineering community as convenient approaches to deal with functions for which evaluations are expensive or noisy, or lack convexity. These methodologies do not typically guarantee any type of convergence under reasonable assumptions.In this paper we will show how to incorporate the use of surrogate models, heuristics, or any other process of attempting a function value decrease in trust-region algorithms for unconstrained derivativefree optimization, in a way that global convergence of the latter algorithms to stationary points is retained. Our approach follows the lines of search/poll direct-search methods and corresponding surrogate management frameworks, both in algorithmic design and in the form of organizing the convergence theory.

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A hybrid surrogate and pattern search optimization method and application to microelectronics

Cited by 27 publications

References 21 publications

Asynchronous parallel hybrid optimization combining DIRECT and GSS

Asynchronous parallel hybrid optimization combining DIRECT and GSS

Sampling-Based RBDO of Ship Hull Structures Considering Thermo-Elasto-Plastic Residual Deformation

A surrogate management framework using rigorous trust-region steps

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