On upper approximations of Pareto fronts

In one of our earlier works, we proposed to approximate Pareto fronts to multiobjective optimization problems by two-sided approximations, one from inside and another from outside of the feasible objective set, called, respectively, lower shell and upper shell. We worked there under the assumption t...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of global optimization 2018-11, Vol.72 (3), p.475-490
Hauptverfasser: Kaliszewski, I., Miroforidis, J.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 490
container_issue 3
container_start_page 475
container_title Journal of global optimization
container_volume 72
creator Kaliszewski, I.
Miroforidis, J.
description In one of our earlier works, we proposed to approximate Pareto fronts to multiobjective optimization problems by two-sided approximations, one from inside and another from outside of the feasible objective set, called, respectively, lower shell and upper shell. We worked there under the assumption that for a given problem an upper shell exists. As it is not always the case, in this paper we give some sufficient conditions for the existence of upper shells. We also investigate how to constructively search infeasible sets to derive upper shells. We approach this issue by means of problem relaxations. We formally show that under certain conditions some subsets of lower shells to relaxed multiobjective optimization problems are upper shells in the respective unrelaxed problems. Results are illustrated by a numerical example representing a small but real mechanical problem. Practical implications of the results are discussed.
doi_str_mv 10.1007/s10898-018-0642-1
format Article
fullrecord <record><control><sourceid>gale_proqu</sourceid><recordid>TN_cdi_proquest_journals_2015563021</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A718423106</galeid><sourcerecordid>A718423106</sourcerecordid><originalsourceid>FETCH-LOGICAL-c355t-4f2c0271117cf0d2569a2384e3be0f3caceee98b94de6cf8d2b9b34fdb8d02153</originalsourceid><addsrcrecordid>eNp1UEtLAzEQDqJgrf4AbwtevGydSTa7ybEUX1CoBz2HbDYpW9pkTbag_96UFTx5GAaG7zUfIbcICwRoHhKCkKIEzFNXtMQzMkPesJJKrM_JDCTlJQfAS3KV0g4ApOB0Ru43vjgOg42FHoYYvvqDHvvgUxFc8aajHUPhYvBjuiYXTu-Tvfndc_Lx9Pi-einXm-fX1XJdGsb5WFaOGqANIjbGQUd5LTVlorKsteCY0cZaK0Urq87WxomOtrJlleta0QFFzubkbtLNaT6PNo1qF47RZ0tFATmvWYZl1GJCbfXeqt67MEadxXVnD70J3ro-35cNiooyhDoTcCKYGFKK1qkh5l_jt0JQpwbV1KDKDapTg-pkQidOyli_tfEvyv-kH5YtchE</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2015563021</pqid></control><display><type>article</type><title>On upper approximations of Pareto fronts</title><source>SpringerLink Journals - AutoHoldings</source><creator>Kaliszewski, I. ; Miroforidis, J.</creator><creatorcontrib>Kaliszewski, I. ; Miroforidis, J.</creatorcontrib><description>In one of our earlier works, we proposed to approximate Pareto fronts to multiobjective optimization problems by two-sided approximations, one from inside and another from outside of the feasible objective set, called, respectively, lower shell and upper shell. We worked there under the assumption that for a given problem an upper shell exists. As it is not always the case, in this paper we give some sufficient conditions for the existence of upper shells. We also investigate how to constructively search infeasible sets to derive upper shells. We approach this issue by means of problem relaxations. We formally show that under certain conditions some subsets of lower shells to relaxed multiobjective optimization problems are upper shells in the respective unrelaxed problems. Results are illustrated by a numerical example representing a small but real mechanical problem. Practical implications of the results are discussed.</description><identifier>ISSN: 0925-5001</identifier><identifier>EISSN: 1573-2916</identifier><identifier>DOI: 10.1007/s10898-018-0642-1</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Approximation ; Computer Science ; Mathematical programming ; Mathematics ; Mathematics and Statistics ; Multiple objective analysis ; Operations Research/Decision Theory ; Optimization ; Pareto optimization ; Pareto optimum ; Real Functions</subject><ispartof>Journal of global optimization, 2018-11, Vol.72 (3), p.475-490</ispartof><rights>The Author(s) 2018</rights><rights>COPYRIGHT 2018 Springer</rights><rights>Journal of Global Optimization is a copyright of Springer, (2018). All Rights Reserved. © 2018. This work is published under http://creativecommons.org/licenses/by/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c355t-4f2c0271117cf0d2569a2384e3be0f3caceee98b94de6cf8d2b9b34fdb8d02153</citedby><cites>FETCH-LOGICAL-c355t-4f2c0271117cf0d2569a2384e3be0f3caceee98b94de6cf8d2b9b34fdb8d02153</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/s10898-018-0642-1$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10898-018-0642-1$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,27922,27923,41486,42555,51317</link.rule.ids></links><search><creatorcontrib>Kaliszewski, I.</creatorcontrib><creatorcontrib>Miroforidis, J.</creatorcontrib><title>On upper approximations of Pareto fronts</title><title>Journal of global optimization</title><addtitle>J Glob Optim</addtitle><description>In one of our earlier works, we proposed to approximate Pareto fronts to multiobjective optimization problems by two-sided approximations, one from inside and another from outside of the feasible objective set, called, respectively, lower shell and upper shell. We worked there under the assumption that for a given problem an upper shell exists. As it is not always the case, in this paper we give some sufficient conditions for the existence of upper shells. We also investigate how to constructively search infeasible sets to derive upper shells. We approach this issue by means of problem relaxations. We formally show that under certain conditions some subsets of lower shells to relaxed multiobjective optimization problems are upper shells in the respective unrelaxed problems. Results are illustrated by a numerical example representing a small but real mechanical problem. Practical implications of the results are discussed.</description><subject>Approximation</subject><subject>Computer Science</subject><subject>Mathematical programming</subject><subject>Mathematics</subject><subject>Mathematics and Statistics</subject><subject>Multiple objective analysis</subject><subject>Operations Research/Decision Theory</subject><subject>Optimization</subject><subject>Pareto optimization</subject><subject>Pareto optimum</subject><subject>Real Functions</subject><issn>0925-5001</issn><issn>1573-2916</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp1UEtLAzEQDqJgrf4AbwtevGydSTa7ybEUX1CoBz2HbDYpW9pkTbag_96UFTx5GAaG7zUfIbcICwRoHhKCkKIEzFNXtMQzMkPesJJKrM_JDCTlJQfAS3KV0g4ApOB0Ru43vjgOg42FHoYYvvqDHvvgUxFc8aajHUPhYvBjuiYXTu-Tvfndc_Lx9Pi-einXm-fX1XJdGsb5WFaOGqANIjbGQUd5LTVlorKsteCY0cZaK0Urq87WxomOtrJlleta0QFFzubkbtLNaT6PNo1qF47RZ0tFATmvWYZl1GJCbfXeqt67MEadxXVnD70J3ro-35cNiooyhDoTcCKYGFKK1qkh5l_jt0JQpwbV1KDKDapTg-pkQidOyli_tfEvyv-kH5YtchE</recordid><startdate>20181101</startdate><enddate>20181101</enddate><creator>Kaliszewski, I.</creator><creator>Miroforidis, J.</creator><general>Springer US</general><general>Springer</general><general>Springer Nature B.V</general><scope>C6C</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SC</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>88I</scope><scope>8AL</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8FL</scope><scope>8G5</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FRNLG</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K60</scope><scope>K6~</scope><scope>K7-</scope><scope>L.-</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0C</scope><scope>M0N</scope><scope>M2O</scope><scope>M2P</scope><scope>M7S</scope><scope>MBDVC</scope><scope>P5Z</scope><scope>P62</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>Q9U</scope></search><sort><creationdate>20181101</creationdate><title>On upper approximations of Pareto fronts</title><author>Kaliszewski, I. ; Miroforidis, J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c355t-4f2c0271117cf0d2569a2384e3be0f3caceee98b94de6cf8d2b9b34fdb8d02153</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Approximation</topic><topic>Computer Science</topic><topic>Mathematical programming</topic><topic>Mathematics</topic><topic>Mathematics and Statistics</topic><topic>Multiple objective analysis</topic><topic>Operations Research/Decision Theory</topic><topic>Optimization</topic><topic>Pareto optimization</topic><topic>Pareto optimum</topic><topic>Real Functions</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kaliszewski, I.</creatorcontrib><creatorcontrib>Miroforidis, J.</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>Research Library (Alumni Edition)</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>Computer Science Database</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ProQuest Engineering Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ABI/INFORM Global</collection><collection>Computing Database</collection><collection>Research Library</collection><collection>Science Database</collection><collection>Engineering Database</collection><collection>Research Library (Corporate)</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>ProQuest Central Basic</collection><jtitle>Journal of global optimization</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kaliszewski, I.</au><au>Miroforidis, J.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>On upper approximations of Pareto fronts</atitle><jtitle>Journal of global optimization</jtitle><stitle>J Glob Optim</stitle><date>2018-11-01</date><risdate>2018</risdate><volume>72</volume><issue>3</issue><spage>475</spage><epage>490</epage><pages>475-490</pages><issn>0925-5001</issn><eissn>1573-2916</eissn><abstract>In one of our earlier works, we proposed to approximate Pareto fronts to multiobjective optimization problems by two-sided approximations, one from inside and another from outside of the feasible objective set, called, respectively, lower shell and upper shell. We worked there under the assumption that for a given problem an upper shell exists. As it is not always the case, in this paper we give some sufficient conditions for the existence of upper shells. We also investigate how to constructively search infeasible sets to derive upper shells. We approach this issue by means of problem relaxations. We formally show that under certain conditions some subsets of lower shells to relaxed multiobjective optimization problems are upper shells in the respective unrelaxed problems. Results are illustrated by a numerical example representing a small but real mechanical problem. Practical implications of the results are discussed.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s10898-018-0642-1</doi><tpages>16</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 0925-5001
ispartof Journal of global optimization, 2018-11, Vol.72 (3), p.475-490
issn 0925-5001
1573-2916
language eng
recordid cdi_proquest_journals_2015563021
source SpringerLink Journals - AutoHoldings
subjects Approximation
Computer Science
Mathematical programming
Mathematics
Mathematics and Statistics
Multiple objective analysis
Operations Research/Decision Theory
Optimization
Pareto optimization
Pareto optimum
Real Functions
title On upper approximations of Pareto fronts
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-13T22%3A06%3A38IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_proqu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=On%20upper%20approximations%20of%20Pareto%20fronts&rft.jtitle=Journal%20of%20global%20optimization&rft.au=Kaliszewski,%20I.&rft.date=2018-11-01&rft.volume=72&rft.issue=3&rft.spage=475&rft.epage=490&rft.pages=475-490&rft.issn=0925-5001&rft.eissn=1573-2916&rft_id=info:doi/10.1007/s10898-018-0642-1&rft_dat=%3Cgale_proqu%3EA718423106%3C/gale_proqu%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2015563021&rft_id=info:pmid/&rft_galeid=A718423106&rfr_iscdi=true