Mixture surrogate models based on Dempster-Shafer theory for global optimization problems

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Mixture surrogate models based on Dempster-Shafer theory for global optimization problems. / Muller, Juliane; Piche, Robert.

In: Journal of Global Optimization, Vol. 51, No. 1, 2011, p. 79-104.

Research output: Contribution to journal › Article › Scientific › peer-review

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@article{c52818bccd644c13bd42c045a24da616,

title = "Mixture surrogate models based on Dempster-Shafer theory for global optimization problems",

abstract = "Recent research in algorithms for solving global optimization problems using response surface methodology has shown that it is in general not possible to use one surrogate model for solving different kinds of problems. In this paper the approach of applying Dempster-Shafer theory to surrogate model selection and their combination is described. Various conflict redistribution rules have been examined with respect to their influence on the results. Furthermore, the implications of the surrogate model type, i.e. using combined, single or a hybrid of both, have been studied. The suggested algorithms were applied to several well-known global optimization test problems. The results indicate that the used approach leads for all problems to a thorough exploration of the variable domain, i.e. the vicinities of global optima could be detected, and that the global minima could in most cases be approximated with high accuracy.",

author = "Juliane Muller and Robert Piche",

note = "Online first<br/>Contribution: organisation=mat,FACT1=1",

year = "2011",

doi = "10.1007/s10898-010-9620-y",

language = "English",

volume = "51",

pages = "79--104",

journal = "Journal of Global Optimization",

issn = "0925-5001",

publisher = "Springer Verlag",

number = "1",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Mixture surrogate models based on Dempster-Shafer theory for global optimization problems

AU - Muller, Juliane

AU - Piche, Robert

N1 - Online first<br/>Contribution: organisation=mat,FACT1=1

PY - 2011

Y1 - 2011

N2 - Recent research in algorithms for solving global optimization problems using response surface methodology has shown that it is in general not possible to use one surrogate model for solving different kinds of problems. In this paper the approach of applying Dempster-Shafer theory to surrogate model selection and their combination is described. Various conflict redistribution rules have been examined with respect to their influence on the results. Furthermore, the implications of the surrogate model type, i.e. using combined, single or a hybrid of both, have been studied. The suggested algorithms were applied to several well-known global optimization test problems. The results indicate that the used approach leads for all problems to a thorough exploration of the variable domain, i.e. the vicinities of global optima could be detected, and that the global minima could in most cases be approximated with high accuracy.

AB - Recent research in algorithms for solving global optimization problems using response surface methodology has shown that it is in general not possible to use one surrogate model for solving different kinds of problems. In this paper the approach of applying Dempster-Shafer theory to surrogate model selection and their combination is described. Various conflict redistribution rules have been examined with respect to their influence on the results. Furthermore, the implications of the surrogate model type, i.e. using combined, single or a hybrid of both, have been studied. The suggested algorithms were applied to several well-known global optimization test problems. The results indicate that the used approach leads for all problems to a thorough exploration of the variable domain, i.e. the vicinities of global optima could be detected, and that the global minima could in most cases be approximated with high accuracy.

U2 - 10.1007/s10898-010-9620-y

DO - 10.1007/s10898-010-9620-y

M3 - Article

VL - 51

SP - 79

EP - 104

JO - Journal of Global Optimization

JF - Journal of Global Optimization

SN - 0925-5001

IS - 1

ER -

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