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Title: A Study For Efficiently Solving Optimisation Problems With An Increasing Number Of Design Variables

Abstract

Coupling optimisation algorithms to Finite Element Methods (FEM) is a very promising way to achieve optimal metal forming processes. However, many optimisation algorithms exist and it is not clear which of these algorithms to use. This paper investigates the sensitivity of a Sequential Approximate Optimisation algorithm (SAO) proposed to an increasing number of design variables and compares it with two other algorithms: an Evolutionary Strategy (ES) and an Evolutionary version of the SAO (ESAO). In addition, it observes the influence of different Designs Of Experiments used with the SAO. It is concluded that the SAO is very capable and efficient and its combination with an ES is not beneficial. Moreover, the use of SAO with Fractional Factorial Design is the most efficient method, rather than Full Factorial Design as proposed.

Authors:
;  [1]; ;  [2]
  1. LTAS-MCT, University of Liege, Chemin des Chevreuils, 1, B-4000 Liege (Belgium)
  2. Faculty of Engineering Technology, University of Twente, P.O. Box 217, 7500 AE Enschede (Netherlands)
Publication Date:
OSTI Identifier:
21061717
Resource Type:
Journal Article
Resource Relation:
Journal Name: AIP Conference Proceedings; Journal Volume: 908; Journal Issue: 1; Conference: NUMIFORM 2007: 9. international conference on numerical methods in industrial forming processes, Porto (Portugal), 17-21 Jun 2007; Other Information: DOI: 10.1063/1.2740857; (c) 2007 American Institute of Physics; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; ALGORITHMS; ALLOYS; COMPARATIVE EVALUATIONS; COMPUTERIZED SIMULATION; DESIGN; ENGINEERING; FINITE ELEMENT METHOD; MATERIALS WORKING; METALS; OPTIMIZATION; SENSITIVITY

Citation Formats

Trichon, S., Ponthot, J.-P., Bonte, M. H. A., and Boogaard, A. H. van den. A Study For Efficiently Solving Optimisation Problems With An Increasing Number Of Design Variables. United States: N. p., 2007. Web. doi:10.1063/1.2740857.
Trichon, S., Ponthot, J.-P., Bonte, M. H. A., & Boogaard, A. H. van den. A Study For Efficiently Solving Optimisation Problems With An Increasing Number Of Design Variables. United States. doi:10.1063/1.2740857.
Trichon, S., Ponthot, J.-P., Bonte, M. H. A., and Boogaard, A. H. van den. Thu . "A Study For Efficiently Solving Optimisation Problems With An Increasing Number Of Design Variables". United States. doi:10.1063/1.2740857.
@article{osti_21061717,
title = {A Study For Efficiently Solving Optimisation Problems With An Increasing Number Of Design Variables},
author = {Trichon, S. and Ponthot, J.-P. and Bonte, M. H. A. and Boogaard, A. H. van den},
abstractNote = {Coupling optimisation algorithms to Finite Element Methods (FEM) is a very promising way to achieve optimal metal forming processes. However, many optimisation algorithms exist and it is not clear which of these algorithms to use. This paper investigates the sensitivity of a Sequential Approximate Optimisation algorithm (SAO) proposed to an increasing number of design variables and compares it with two other algorithms: an Evolutionary Strategy (ES) and an Evolutionary version of the SAO (ESAO). In addition, it observes the influence of different Designs Of Experiments used with the SAO. It is concluded that the SAO is very capable and efficient and its combination with an ES is not beneficial. Moreover, the use of SAO with Fractional Factorial Design is the most efficient method, rather than Full Factorial Design as proposed.},
doi = {10.1063/1.2740857},
journal = {AIP Conference Proceedings},
number = 1,
volume = 908,
place = {United States},
year = {Thu May 17 00:00:00 EDT 2007},
month = {Thu May 17 00:00:00 EDT 2007}
}