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    An Adaptive Sequential Linear Programming Algorithm for Optimal Design Problems With Probabilistic Constraints

    Source: Journal of Mechanical Design:;2007:;volume( 129 ):;issue: 002::page 140
    Author:
    Kuei-Yuan Chan
    ,
    Steven J. Skerlos
    ,
    Panos Papalambros
    DOI: 10.1115/1.2337312
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Optimal design problems with probabilistic constraints, often referred to as reliability-based design optimization problems, have been the subject of extensive recent studies. Solution methods to date have focused more on improving efficiency rather than accuracy and the global convergence behavior of the solution. A new strategy utilizing an adaptive sequential linear programming (SLP) algorithm is proposed as a promising approach to balance accuracy, efficiency, and convergence. The strategy transforms the nonlinear probabilistic constraints into equivalent deterministic ones using both first order and second order approximations, and applies a filter-based SLP algorithm to reach the optimum. Simple numerical examples show promise for increased accuracy without sacrificing efficiency.
    keyword(s): Algorithms , Design , Filters AND Linear programming ,
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      An Adaptive Sequential Linear Programming Algorithm for Optimal Design Problems With Probabilistic Constraints

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/136519
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    • Journal of Mechanical Design

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    contributor authorKuei-Yuan Chan
    contributor authorSteven J. Skerlos
    contributor authorPanos Papalambros
    date accessioned2017-05-09T00:25:11Z
    date available2017-05-09T00:25:11Z
    date copyrightFebruary, 2007
    date issued2007
    identifier issn1050-0472
    identifier otherJMDEDB-27842#140_1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/136519
    description abstractOptimal design problems with probabilistic constraints, often referred to as reliability-based design optimization problems, have been the subject of extensive recent studies. Solution methods to date have focused more on improving efficiency rather than accuracy and the global convergence behavior of the solution. A new strategy utilizing an adaptive sequential linear programming (SLP) algorithm is proposed as a promising approach to balance accuracy, efficiency, and convergence. The strategy transforms the nonlinear probabilistic constraints into equivalent deterministic ones using both first order and second order approximations, and applies a filter-based SLP algorithm to reach the optimum. Simple numerical examples show promise for increased accuracy without sacrificing efficiency.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleAn Adaptive Sequential Linear Programming Algorithm for Optimal Design Problems With Probabilistic Constraints
    typeJournal Paper
    journal volume129
    journal issue2
    journal titleJournal of Mechanical Design
    identifier doi10.1115/1.2337312
    journal fristpage140
    journal lastpage149
    identifier eissn1528-9001
    keywordsAlgorithms
    keywordsDesign
    keywordsFilters AND Linear programming
    treeJournal of Mechanical Design:;2007:;volume( 129 ):;issue: 002
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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