Partial design space exploration strategies applied in preliminary design

During preliminary phases in product design, on the basis of strong physical hypotheses (e.g. isotherm, steady state), physical and functional requirements can be expressed as coarse-grained constraint-based models on a few degrees of freedom, possibly including several design criteria to optimize....

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Published in:	International journal on interactive design and manufacturing Vol. 18; no. 4; pp. 2291 - 2307
Main Authors:	Richard de Latour, Thomas, Chenouard, Raphaël, Granvilliers, Laurent
Format:	Journal Article
Language:	English
Published:	Paris Springer Paris 01.05.2024 Springer Nature B.V Springer Verlag
Subjects:	Algorithms Boxes CAE) and Design Computer Science Computer-Aided Engineering (CAD Decision making Design Design criteria Design engineering Design optimization Electronics and Microelectronics Energy consumption Engineering Engineering Design Engineering Sciences Genetic algorithms Hypotheses Industrial Design Instrumentation Mechanical Engineering Multiple objective analysis Optimization Original Paper Preliminary designs Product design Solvers Space exploration Variables Numerical constraint satisfaction problem Preliminary design Anytime branch-and-prune algorithm Multi-objective optimization Design space exploration Design space exploration numerical constraint satisfaction problem anytime branch-and-prune algorithm multi-objective optimization preliminary design anytime branch-and-prune algorithm multi-objective optimization preliminary design numerical constraint satisfaction problem
ISSN:	1955-2513, 1955-2505
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Abstract	During preliminary phases in product design, on the basis of strong physical hypotheses (e.g. isotherm, steady state), physical and functional requirements can be expressed as coarse-grained constraint-based models on a few degrees of freedom, possibly including several design criteria to optimize. Such models are usually handled by multi-objective optimization solvers in order to find design solutions giving the best trade-offs between design criteria. Another approach developed in this paper is to partially explore all the areas of the design space using an anytime interval branch-and-prune algorithm called IDFS such that the design criteria are converted into so-called ε -constraints. The expected result is a sample of solutions diversified in both the objective space and the design space. Several quality indicators are introduced in order to measure this diversity and compare IDFS with two state-of-the-art multi-objective optimization solvers NSGA-II and NSGA-III on three real-world case studies. The results show that IDFS is able to identify new close-to-optimal designs and permits a better understanding of the design space. This framework provides a promising alternative tool for decision making, in particular for integrating interaction in the preliminary design process. Graphical Abstract Partial exploration aims to compute a diversified subset of feasible solutions; We built an anytime branch and prune algorithm for partial design space exploration. We built a protocol to analyze diversity in both the design and the objective space. We compare partial exploration and optimization approaches on three design problems. Partial Exploration is a tool for decision makers to identify quasi-optimal designs.
AbstractList	During preliminary phases in product design, on the basis of strong physical hypotheses (e.g. isotherm, steady state), physical and functional requirements can be expressed as coarse-grained constraint-based models on a few degrees of freedom, possibly including several design criteria to optimize. Such models are usually handled by multi-objective optimization solvers in order to find design solutions giving the best trade-offs between design criteria. Another approach developed in this paper is to partially explore all the areas of the design space using an anytime interval branch-and-prune algorithm called IDFS such that the design criteria are converted into so-called ε-constraints. The expected result is a sample of solutions diversified in both the objective space and the design space. Several quality indicators are introduced in order to measure this diversity and compare IDFS with two state-of-the-art multi-objective optimization solvers NSGA-II and NSGA-III on three real-world case studies. The results show that IDFS is able to identify new close-to-optimal designs and permits a better understanding of the design space. This framework provides a promising alternative tool for decision making, in particular for integrating interaction in the preliminary design process.Partial exploration aims to compute a diversified subset of feasible solutions; We built an anytime branch and prune algorithm for partial design space exploration. We built a protocol to analyze diversity in both the design and the objective space. We compare partial exploration and optimization approaches on three design problems. Partial Exploration is a tool for decision makers to identify quasi-optimal designs. During preliminary phases in product design, on the basis of strong physical hypotheses (e.g. isotherm, steady state), physical and functional requirements can be expressed as coarse-grained constraintbased models on a few degrees of freedom, possibly including several design criteria to optimize. Such models are usually handled by multi-objective optimization solvers in order to find design solutions giving the best trade-o↵s between design criteria. Another approach developed in this paper is to partially explore all the areas of the design space using an anytime interval branch-and-prune algorithm called IDFS such that the design criteria are converted into so-called "-constraints. The expected result is a sample of solutions diversified in both the objective space and the design space. Several quality indicators are introduced in order to measure this diversity and compare IDFS with two state-ofthe-art multi-objective optimization solvers NSGA-II and NSGA-III on three real-world case studies. The results show that IDFS is able to identify new close-to-optimal designs and permits a better understanding of the design space. This framework provides a promising alternative tool for decision making, in particular for integrating interaction in the preliminary design process. During preliminary phases in product design, on the basis of strong physical hypotheses (e.g. isotherm, steady state), physical and functional requirements can be expressed as coarse-grained constraint-based models on a few degrees of freedom, possibly including several design criteria to optimize. Such models are usually handled by multi-objective optimization solvers in order to find design solutions giving the best trade-offs between design criteria. Another approach developed in this paper is to partially explore all the areas of the design space using an anytime interval branch-and-prune algorithm called IDFS such that the design criteria are converted into so-called ε -constraints. The expected result is a sample of solutions diversified in both the objective space and the design space. Several quality indicators are introduced in order to measure this diversity and compare IDFS with two state-of-the-art multi-objective optimization solvers NSGA-II and NSGA-III on three real-world case studies. The results show that IDFS is able to identify new close-to-optimal designs and permits a better understanding of the design space. This framework provides a promising alternative tool for decision making, in particular for integrating interaction in the preliminary design process. Graphical Abstract Partial exploration aims to compute a diversified subset of feasible solutions; We built an anytime branch and prune algorithm for partial design space exploration. We built a protocol to analyze diversity in both the design and the objective space. We compare partial exploration and optimization approaches on three design problems. Partial Exploration is a tool for decision makers to identify quasi-optimal designs.
Author	Richard de Latour, Thomas Granvilliers, Laurent Chenouard, Raphaël
Author_xml	– sequence: 1 givenname: Thomas orcidid: 0000-0003-0341-6709 surname: Richard de Latour fullname: Richard de Latour, Thomas email: thomas.richard-de-latour@ls2n.fr organization: LS2N, UMR6004, Nantes Université, École Centrale Nantes, CNRS – sequence: 2 givenname: Raphaël orcidid: 0000-0002-9761-6703 surname: Chenouard fullname: Chenouard, Raphaël organization: LS2N, UMR6004, Nantes Université, École Centrale Nantes, CNRS – sequence: 3 givenname: Laurent orcidid: 0000-0003-3320-7502 surname: Granvilliers fullname: Granvilliers, Laurent organization: LS2N, UMR6004, Nantes Université, École Centrale Nantes, CNRS
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Cites_doi	10.1016/j.tcs.2005.09.022 10.1007/s10957-008-9394-2 10.1007/s12008-020-00679-4 10.1007/PL00007190 10.1007/BF00138689 10.1109/TEVC.2017.2672668 10.1016/j.disopt.2016.01.005 10.1007/s00158-013-0996-4 10.1016/j.artint.2009.03.002 10.1109/TEVC.2013.2281534 10.5555/1622270.1622355 10.1016/j.ejor.2020.11.016 10.7551/mitpress/4304.003.0024 10.1007/s10898-014-0166-2 10.1017/S0890060409000055 10.1007/s101070050011 10.1007/s12008-018-0477-5 10.1016/j.asoc.2015.04.010 10.1145/3828.3830 10.1007/s12008-020-00685-6 10.1109/20.650361 10.1007/s12008-008-0047-3 10.1007/s12008-020-00680-x 10.1016/j.pnucene.2021.103830 10.1609/aaai.v34i02.5512 10.1016/j.jclepro.2012.10.048 10.1007/978-3-642-21292-5_3 10.1137/S0036142995281504 10.1007/978-3-642-27937-9 10.1007/3-540-45356-3_83 10.1017/9781316451038 10.1145/774801.774804 10.1109/ICCIE.2009.5223678 10.1109/TEVC.2003.810758
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Keywords	Numerical constraint satisfaction problem Preliminary design Anytime branch-and-prune algorithm Multi-objective optimization Design space exploration Design space exploration numerical constraint satisfaction problem anytime branch-and-prune algorithm multi-objective optimization preliminary design anytime branch-and-prune algorithm multi-objective optimization preliminary design numerical constraint satisfaction problem
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Title	Partial design space exploration strategies applied in preliminary design
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