Combining Global and Local Surrogate Models to Accelerate Evolutionary Optimization

Zhou, Zongzhao; Ong, Yew Soon; Nair, Prasanth B.; Keane, Andy J.; Lum, Kai‐Yew

doi:10.1109/tsmcc.2005.855506

Cited by 369 publications

(174 citation statements)

References 18 publications

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“…Kogiso et al (1994) created derivativebased local approximation near each member of the population for accelerating the convergence of genetic algorithms. The class of algorithms that combine evolutionary algorithms with local search are now known as memetic algorithms (Hart et al 2005, Zhou et al 2007a. Sun et al (2015) combine a global surrogate with local surrogates near particles for particle swarm optimization.…”

Section: Local Searchesmentioning

confidence: 99%

Parallel surrogate-assisted global optimization with expensive functions – a survey

Haftka

Villanueva

Chaudhuri

2016

Struct Multidisc Optim

210

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Surrogate assisted global optimization is gaining popularity. Similarly, modern advances in computing power increasingly rely on parallelization rather than faster processors. This paper examines some of the methods used to take advantage of parallelization in surrogate based global optimization. A key issue focused on in this review is how different algorithms balance exploration and exploitation. Most of the papers surveyed are adaptive samplers that employ Gaussian Process or Kriging surrogates. These allow sophisticated approaches for balancing exploration and exploitation and even allow to develop algorithms with calculable rate of convergence as function of the number of parallel processors. In addition to optimization based on adaptive sampling, surrogate assisted parallel evolutionary algorithms are also surveyed. Beyond a review of the present state of the art, the paper also argues that methods that provide easy parallelization, like multiple parallel runs, or methods that rely on population of designs for diversity deserve more attention.

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Section: Local Searchesmentioning

confidence: 99%

Parallel surrogate-assisted global optimization with expensive functions – a survey

Haftka

Villanueva

Chaudhuri

2016

Struct Multidisc Optim

210

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“…Surrogate models have been incorporated in the optimization process in several ways. They can guide the global search and/or serve as local model for evolutionary optimization algorithms [24,33]. Surrogate models can also be applied in a trust region model-management framework to optimize systems with complex local behaviour [1].…”

Section: Surrogate-based Optimizationmentioning

confidence: 99%

Inverse modelling of an aneurysm’s stiffness using surrogate-based optimization and fluid-structure interaction simulations

Degroote

Couckuyt

Vierendeels

et al. 2012

Struct Multidisc Optim

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Characterization of the mechanical properties of arterial tissues is highly relevant. In this work, we apply an inverse modelling approach to a model accounting for an aneurysm and the distal part of the circulation which can be modified using two independent stiffness parameters. For given values of these parameters, the position of the arterial wall as a function of time is calculated using a forward simulation which takes the fluid-structure interaction (FSI) into account. Using this forward simulation, the correct values of the stiffness parameters are obtained by minimizing a cost function, which is defined as the difference between the forward simulation and a measurement. The minimization is performed by means of surrogate-based optimization using a Kriging model combined with the expected improvement infill criterion. The results show that the stiffness parameters converge to the correct values, both for a zero-dimensional and for a three-dimensional model of the aneurysm.

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“…For instance, Zhou et al [11] apply a data parallel Gaussian Process for the global approximation and a (simple) Radial Basis Function (RBF) model for the local search. Lim et al [12] benchmark different local surrogate modeling techniques (quadratic polynomials, GP, RBF, and extreme learning machine neural networks) including the use of (fixed) ensembles, in combination with evolutionary computation.…”

Section: Surrogate-based Optimization (Sbo)mentioning

confidence: 99%

Surrogate-based infill optimization applied to electromagnetic problems

Couckuyt

Declercq

Dhaene

et al. 2010

Int J RF and Microwave Comp Aid Eng

129

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The increasing use of expensive computer simulations in engineering places a serious computational burden on associated optimization problems. Surrogate-based optimization becomes standard practice in analyzing such expensive black-box problems. This article discusses several approaches that use surrogate models for optimization and highlights one sequential design approach in particular, namely, expected improvement. The expected improvement approach is demonstrated on two electromagnetic problems, namely, a microwave filter and a textile antenna.

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Combining Global and Local Surrogate Models to Accelerate Evolutionary Optimization

Cited by 369 publications

References 18 publications

Parallel surrogate-assisted global optimization with expensive functions – a survey

Parallel surrogate-assisted global optimization with expensive functions – a survey

Inverse modelling of an aneurysm’s stiffness using surrogate-based optimization and fluid-structure interaction simulations

Surrogate-based infill optimization applied to electromagnetic problems

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