Applicability of surrogates to improve efficiency of particle swarm optimization for simulation-based problems

Parno, Matthew; Hemker, Thomas; Fowler, Kathleen

doi:10.1080/0305215x.2011.598521

Cited by 40 publications

(14 citation statements)

References 25 publications

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“…Ratle 2001, Won and Ray 2005, Emmerich, Giannakoglou, and Naujoks 2006and Annicchiarico 2007, and particle swarm optimization algorithms (e.g. Parno, Hemker, and Fowler 2012). Downloaded by [University of Nebraska, Lincoln] at 17:02 29 September 2015…”

Section: Related Workmentioning

confidence: 99%

Trust regions in Kriging-based optimization with expected improvement

Regis

2015

Engineering Optimization

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The Kriging-based Efficient Global Optimization (EGO) method works well on many expensive blackbox optimization problems. However, it does not seem to perform well on problems with steep and narrow global minimum basins and on high-dimensional problems. This article develops a new Krigingbased optimization method called TRIKE (Trust Region Implementation in Kriging-based optimization with Expected improvement) that implements a trust-region-like approach where each iterate is obtained by maximizing an Expected Improvement (EI) function within some trust region. This trust region is adjusted depending on the ratio of the actual improvement to the EI. This article also develops the Kriging-based CYCLONE (CYClic Local search in OptimizatioN using Expected improvement) method that uses a cyclic pattern to determine the search regions where the EI is maximized. TRIKE and CYCLONE are compared with EGO on 28 test problems with up to 32 dimensions and on a 36-dimensional groundwater bioremediation application in appendices supplied as an online supplement available at http://dx.. The results show that both algorithms yield substantial improvements over EGO and they are competitive with a radial basis function method.

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Section: Related Workmentioning

confidence: 99%

Trust regions in Kriging-based optimization with expected improvement

Regis

2015

Engineering Optimization

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“…The results of traditional PSO are affected by the particles' initial values, and a local minimum or premature convergence is often obtained [17,32,33]. Thus, using a genetic algorithm, a nonlinear mutation was added after the particle acceleration.…”

Section: Mutation Particle Swarm Optimizationmentioning

confidence: 99%

Back-analysis of soil parameters of the Malutang II concrete face rockfill dam using parallel mutation particle swarm optimization

Jia

Chi

2015

Computers and Geotechnics

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“…There are many ways the surrogates may be used along with exact function evaluations and Jin (2005 and2011) provides a good survey of different methods. The idea of using surrogates to improve the efficiency of evolutionary methods continues with recent interest in application to particle swarm optimization (e.g., Parno et al 2012;Regis 2014;Sun et al 2015) and evolutionary programming (Regis 2015). Though Jin's paper on surrogate-assisted evolutionary algorithms as well as more recent papers on particle swarm optimization do not specifically mention parallelization, as many have pointed out, the mechanisms behind natureinspired algorithms easily lend themselves to parallelization.…”

Section: Parallel Surrogate-assisted Evolutionary Algorithmsmentioning

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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Applicability of surrogates to improve efficiency of particle swarm optimization for simulation-based problems

Cited by 40 publications

References 25 publications

Trust regions in Kriging-based optimization with expected improvement

Trust regions in Kriging-based optimization with expected improvement

Back-analysis of soil parameters of the Malutang II concrete face rockfill dam using parallel mutation particle swarm optimization

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

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