Applied soft computing for optimum design of structures

In this study a critical assessment of three metaheuristic optimization algorithms, namely differential evolution, harmony search and particle swarm optimization, is performed with reference to their efficiency and robustness for the optimum design of real-world structures. Furthermore, a neural net...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Structural and multidisciplinary optimization 2012-06, Vol.45 (6), p.787-799
Hauptverfasser: Lagaros, Nikos D., Papadrakakis, Manolis
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 799
container_issue 6
container_start_page 787
container_title Structural and multidisciplinary optimization
container_volume 45
creator Lagaros, Nikos D.
Papadrakakis, Manolis
description In this study a critical assessment of three metaheuristic optimization algorithms, namely differential evolution, harmony search and particle swarm optimization, is performed with reference to their efficiency and robustness for the optimum design of real-world structures. Furthermore, a neural network based prediction scheme of the structural response, required to assess the quality of each candidate design during the optimization procedure, is proposed. The proposed methodology is applied to an overhead crane structure using different finite element simulations corresponding to a solid discretization as well as mixed discretizations with shell-solid and beam-solid elements. The number of degrees of freedom (dof) resulted for the simulation of the structural response varies in the range of 60,000 to 1,400,000 dof leading to highly computational intensive problems.
doi_str_mv 10.1007/s00158-011-0741-9
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2262604020</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2262604020</sourcerecordid><originalsourceid>FETCH-LOGICAL-c316t-262df5fc37d02ac10ad3882b49419d51288eb6c4aa289533af232503291f26a13</originalsourceid><addsrcrecordid>eNp1kMFKxDAURYMoOI5-gLuA6-h7Sdqmy2FQRxhwo-AuZNJk6DBtapIu_Hs7VHTl6t3FPffBIeQW4R4BqocEgIVigMigksjqM7LAEguGUqnz31x9XJKrlA4AoEDWC1KthuHYuoam4DO1oRvG3PZ76kOkYchtN3a0cand9zR4mnIcbR6jS9fkwptjcjc_d0nenx7f1hu2fX1-Wa-2zAosM-Mlb3zhraga4MYimEYoxXeyllg3BXKl3K600hiu6kII47ngBQheo-elQbEkd_PuEMPn6FLWhzDGfnqp-TReggQOUwvnlo0hpei8HmLbmfilEfTJj5796MmPPvnR9cTwmUlTt9-7-Lf8P_QNRhRmnQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2262604020</pqid></control><display><type>article</type><title>Applied soft computing for optimum design of structures</title><source>SpringerLink Journals</source><creator>Lagaros, Nikos D. ; Papadrakakis, Manolis</creator><creatorcontrib>Lagaros, Nikos D. ; Papadrakakis, Manolis</creatorcontrib><description>In this study a critical assessment of three metaheuristic optimization algorithms, namely differential evolution, harmony search and particle swarm optimization, is performed with reference to their efficiency and robustness for the optimum design of real-world structures. Furthermore, a neural network based prediction scheme of the structural response, required to assess the quality of each candidate design during the optimization procedure, is proposed. The proposed methodology is applied to an overhead crane structure using different finite element simulations corresponding to a solid discretization as well as mixed discretizations with shell-solid and beam-solid elements. The number of degrees of freedom (dof) resulted for the simulation of the structural response varies in the range of 60,000 to 1,400,000 dof leading to highly computational intensive problems.</description><identifier>ISSN: 1615-147X</identifier><identifier>EISSN: 1615-1488</identifier><identifier>DOI: 10.1007/s00158-011-0741-9</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer-Verlag</publisher><subject>Computational Mathematics and Numerical Analysis ; Computer simulation ; Cranes ; Design optimization ; Engineering ; Engineering Design ; Evolutionary algorithms ; Evolutionary computation ; Finite element method ; Heuristic methods ; Neural networks ; Particle swarm optimization ; Quality assessment ; Research Paper ; Soft computing ; Theoretical and Applied Mechanics</subject><ispartof>Structural and multidisciplinary optimization, 2012-06, Vol.45 (6), p.787-799</ispartof><rights>Springer-Verlag 2011</rights><rights>Structural and Multidisciplinary Optimization is a copyright of Springer, (2011). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c316t-262df5fc37d02ac10ad3882b49419d51288eb6c4aa289533af232503291f26a13</citedby><cites>FETCH-LOGICAL-c316t-262df5fc37d02ac10ad3882b49419d51288eb6c4aa289533af232503291f26a13</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00158-011-0741-9$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00158-011-0741-9$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27903,27904,41467,42536,51297</link.rule.ids></links><search><creatorcontrib>Lagaros, Nikos D.</creatorcontrib><creatorcontrib>Papadrakakis, Manolis</creatorcontrib><title>Applied soft computing for optimum design of structures</title><title>Structural and multidisciplinary optimization</title><addtitle>Struct Multidisc Optim</addtitle><description>In this study a critical assessment of three metaheuristic optimization algorithms, namely differential evolution, harmony search and particle swarm optimization, is performed with reference to their efficiency and robustness for the optimum design of real-world structures. Furthermore, a neural network based prediction scheme of the structural response, required to assess the quality of each candidate design during the optimization procedure, is proposed. The proposed methodology is applied to an overhead crane structure using different finite element simulations corresponding to a solid discretization as well as mixed discretizations with shell-solid and beam-solid elements. The number of degrees of freedom (dof) resulted for the simulation of the structural response varies in the range of 60,000 to 1,400,000 dof leading to highly computational intensive problems.</description><subject>Computational Mathematics and Numerical Analysis</subject><subject>Computer simulation</subject><subject>Cranes</subject><subject>Design optimization</subject><subject>Engineering</subject><subject>Engineering Design</subject><subject>Evolutionary algorithms</subject><subject>Evolutionary computation</subject><subject>Finite element method</subject><subject>Heuristic methods</subject><subject>Neural networks</subject><subject>Particle swarm optimization</subject><subject>Quality assessment</subject><subject>Research Paper</subject><subject>Soft computing</subject><subject>Theoretical and Applied Mechanics</subject><issn>1615-147X</issn><issn>1615-1488</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNp1kMFKxDAURYMoOI5-gLuA6-h7Sdqmy2FQRxhwo-AuZNJk6DBtapIu_Hs7VHTl6t3FPffBIeQW4R4BqocEgIVigMigksjqM7LAEguGUqnz31x9XJKrlA4AoEDWC1KthuHYuoam4DO1oRvG3PZ76kOkYchtN3a0cand9zR4mnIcbR6jS9fkwptjcjc_d0nenx7f1hu2fX1-Wa-2zAosM-Mlb3zhraga4MYimEYoxXeyllg3BXKl3K600hiu6kII47ngBQheo-elQbEkd_PuEMPn6FLWhzDGfnqp-TReggQOUwvnlo0hpei8HmLbmfilEfTJj5796MmPPvnR9cTwmUlTt9-7-Lf8P_QNRhRmnQ</recordid><startdate>20120601</startdate><enddate>20120601</enddate><creator>Lagaros, Nikos D.</creator><creator>Papadrakakis, Manolis</creator><general>Springer-Verlag</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20120601</creationdate><title>Applied soft computing for optimum design of structures</title><author>Lagaros, Nikos D. ; Papadrakakis, Manolis</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c316t-262df5fc37d02ac10ad3882b49419d51288eb6c4aa289533af232503291f26a13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Computational Mathematics and Numerical Analysis</topic><topic>Computer simulation</topic><topic>Cranes</topic><topic>Design optimization</topic><topic>Engineering</topic><topic>Engineering Design</topic><topic>Evolutionary algorithms</topic><topic>Evolutionary computation</topic><topic>Finite element method</topic><topic>Heuristic methods</topic><topic>Neural networks</topic><topic>Particle swarm optimization</topic><topic>Quality assessment</topic><topic>Research Paper</topic><topic>Soft computing</topic><topic>Theoretical and Applied Mechanics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lagaros, Nikos D.</creatorcontrib><creatorcontrib>Papadrakakis, Manolis</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><jtitle>Structural and multidisciplinary optimization</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lagaros, Nikos D.</au><au>Papadrakakis, Manolis</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Applied soft computing for optimum design of structures</atitle><jtitle>Structural and multidisciplinary optimization</jtitle><stitle>Struct Multidisc Optim</stitle><date>2012-06-01</date><risdate>2012</risdate><volume>45</volume><issue>6</issue><spage>787</spage><epage>799</epage><pages>787-799</pages><issn>1615-147X</issn><eissn>1615-1488</eissn><abstract>In this study a critical assessment of three metaheuristic optimization algorithms, namely differential evolution, harmony search and particle swarm optimization, is performed with reference to their efficiency and robustness for the optimum design of real-world structures. Furthermore, a neural network based prediction scheme of the structural response, required to assess the quality of each candidate design during the optimization procedure, is proposed. The proposed methodology is applied to an overhead crane structure using different finite element simulations corresponding to a solid discretization as well as mixed discretizations with shell-solid and beam-solid elements. The number of degrees of freedom (dof) resulted for the simulation of the structural response varies in the range of 60,000 to 1,400,000 dof leading to highly computational intensive problems.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer-Verlag</pub><doi>10.1007/s00158-011-0741-9</doi><tpages>13</tpages></addata></record>
fulltext fulltext
identifier ISSN: 1615-147X
ispartof Structural and multidisciplinary optimization, 2012-06, Vol.45 (6), p.787-799
issn 1615-147X
1615-1488
language eng
recordid cdi_proquest_journals_2262604020
source SpringerLink Journals
subjects Computational Mathematics and Numerical Analysis
Computer simulation
Cranes
Design optimization
Engineering
Engineering Design
Evolutionary algorithms
Evolutionary computation
Finite element method
Heuristic methods
Neural networks
Particle swarm optimization
Quality assessment
Research Paper
Soft computing
Theoretical and Applied Mechanics
title Applied soft computing for optimum design of structures
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-25T15%3A06%3A43IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Applied%20soft%20computing%20for%20optimum%20design%20of%20structures&rft.jtitle=Structural%20and%20multidisciplinary%20optimization&rft.au=Lagaros,%20Nikos%20D.&rft.date=2012-06-01&rft.volume=45&rft.issue=6&rft.spage=787&rft.epage=799&rft.pages=787-799&rft.issn=1615-147X&rft.eissn=1615-1488&rft_id=info:doi/10.1007/s00158-011-0741-9&rft_dat=%3Cproquest_cross%3E2262604020%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2262604020&rft_id=info:pmid/&rfr_iscdi=true