Pareto Set Computation in Convex Multi-objective Design using Adaptive Response Surface Method (ARSM)

Optimal design problems normally involve high dimensional design spaces and multiple objective functions. Depending on the complexity of the model, the time required to explore the design space could become excessive. This paper describes the calculation of the Pareto-optimal set based on adaptive s...
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Autor*in:	Panesso Miguel A. [verfasserIn] Cruz Camilo J. [verfasserIn] Bohorquez Juan C. [verfasserIn] Muñóz Luis E. [verfasserIn] Peña Néstor M. [verfasserIn] Segura Fredy E. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch ; Französisch

Erschienen:	2016

Übergeordnetes Werk:	In: MATEC Web of Conferences - EDP Sciences, 2013, 42, p 05003(2016)
Übergeordnetes Werk:	volume:42, p 05003 ; year:2016

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1051/matecconf/20164205003

Katalog-ID:	DOAJ071872655

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allfieldsSound	10.1051/matecconf/20164205003 doi (DE-627)DOAJ071872655 (DE-599)DOAJ8ea60eb0cee345d3ad8a7b0dff28338f DE-627 ger DE-627 rakwb eng fre TA1-2040 Panesso Miguel A. verfasserin aut Pareto Set Computation in Convex Multi-objective Design using Adaptive Response Surface Method (ARSM) 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Optimal design problems normally involve high dimensional design spaces and multiple objective functions. Depending on the complexity of the model, the time required to explore the design space could become excessive. This paper describes the calculation of the Pareto-optimal set based on adaptive surface methodology (ARSM) in order to reduce simulation times given a finite element analysis (FEA) simulation model. The Pareto-optimal strategy consists in the solution of a set of different single-objective problems. Each of this points is found via ARSM. The implementation of ARSM aims to use a few initial simulation points to approximate accurately the set of single-objective functions required. The methodology reduces significantly the number of points required to compute the efficient set compared to other strategies (e.g the exhaustive method), proving to reduce the simulation time of a computationally intensive model. Engineering (General). Civil engineering (General) Cruz Camilo J. verfasserin aut Bohorquez Juan C. verfasserin aut Muñóz Luis E. verfasserin aut Peña Néstor M. verfasserin aut Segura Fredy E. verfasserin aut In MATEC Web of Conferences EDP Sciences, 2013 42, p 05003(2016) (DE-627)720166209 (DE-600)2673602-0 2261236X nnns volume:42, p 05003 year:2016 https://doi.org/10.1051/matecconf/20164205003 kostenfrei https://doaj.org/article/8ea60eb0cee345d3ad8a7b0dff28338f kostenfrei http://dx.doi.org/10.1051/matecconf/20164205003 kostenfrei https://doaj.org/toc/2261-236X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 42, p 05003 2016
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title	Pareto Set Computation in Convex Multi-objective Design using Adaptive Response Surface Method (ARSM)
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title_auth	Pareto Set Computation in Convex Multi-objective Design using Adaptive Response Surface Method (ARSM)
abstract	Optimal design problems normally involve high dimensional design spaces and multiple objective functions. Depending on the complexity of the model, the time required to explore the design space could become excessive. This paper describes the calculation of the Pareto-optimal set based on adaptive surface methodology (ARSM) in order to reduce simulation times given a finite element analysis (FEA) simulation model. The Pareto-optimal strategy consists in the solution of a set of different single-objective problems. Each of this points is found via ARSM. The implementation of ARSM aims to use a few initial simulation points to approximate accurately the set of single-objective functions required. The methodology reduces significantly the number of points required to compute the efficient set compared to other strategies (e.g the exhaustive method), proving to reduce the simulation time of a computationally intensive model.
abstractGer	Optimal design problems normally involve high dimensional design spaces and multiple objective functions. Depending on the complexity of the model, the time required to explore the design space could become excessive. This paper describes the calculation of the Pareto-optimal set based on adaptive surface methodology (ARSM) in order to reduce simulation times given a finite element analysis (FEA) simulation model. The Pareto-optimal strategy consists in the solution of a set of different single-objective problems. Each of this points is found via ARSM. The implementation of ARSM aims to use a few initial simulation points to approximate accurately the set of single-objective functions required. The methodology reduces significantly the number of points required to compute the efficient set compared to other strategies (e.g the exhaustive method), proving to reduce the simulation time of a computationally intensive model.
abstract_unstemmed	Optimal design problems normally involve high dimensional design spaces and multiple objective functions. Depending on the complexity of the model, the time required to explore the design space could become excessive. This paper describes the calculation of the Pareto-optimal set based on adaptive surface methodology (ARSM) in order to reduce simulation times given a finite element analysis (FEA) simulation model. The Pareto-optimal strategy consists in the solution of a set of different single-objective problems. Each of this points is found via ARSM. The implementation of ARSM aims to use a few initial simulation points to approximate accurately the set of single-objective functions required. The methodology reduces significantly the number of points required to compute the efficient set compared to other strategies (e.g the exhaustive method), proving to reduce the simulation time of a computationally intensive model.
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title_short	Pareto Set Computation in Convex Multi-objective Design using Adaptive Response Surface Method (ARSM)
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Pareto Set Computation in Convex Multi-objective Design using Adaptive Response Surface Method (ARSM)

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