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Optimal shape design of a class of permanent magnet motors in a multiple-objectives context
Purpose This paper aims to deal with the optimal shape design of a class of permanent magnet motors by minimizing multiple objectives according to an original interpretation of Pareto optimality. The proposed method solves a many-objective problems characterized by five objective functions and five...
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Published in: | Compel 2022-10, Vol.41 (6), p.1994-2009 |
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container_end_page | 2009 |
container_issue | 6 |
container_start_page | 1994 |
container_title | Compel |
container_volume | 41 |
creator | Di Barba, Paolo Mognaschi, Maria Evelina Petkovska, Lidija Cvetkovski, Goga Vladimir |
description | Purpose
This paper aims to deal with the optimal shape design of a class of permanent magnet motors by minimizing multiple objectives according to an original interpretation of Pareto optimality. The proposed method solves a many-objective problems characterized by five objective functions and five design variables with evolution strategy algorithms, classically used for single- and multi-objective (two objective functions) optimization problems.
Design/methodology/approach
Two approaches are proposed in the paper: the All-Objectives (AO) and the Many-Objectives (MO) optimization approach. The former is based on a single-objective optimization of a preference function, i.e. a normalized weighted sum. In contrast, in the MO a multi-objective optimization algorithm is applied to the minimization of a weight-free preference function and simultaneously to a maximization of the distance of the current solution from the prototype. The optimizations are based on an equivalent circuit model of the Permanent Magnet (PM) motor, but the results are assessed by means of finite element analyses (FEAs).
Findings
An extensive study of the solutions obtained by means of the different optimization approaches is provided by means of post-processing analyses. Both the approaches find non-dominated solutions with respect to the prototype that are substantially improving the initial solution. The points of strength along with the weakness points of each solution with respect to the prototype are analysed in depth.
Practical implications
The paper gives a good guide to the designers of electric motors, focussed on a shape design optimization.
Originality/value
Considering simultaneously five objective functions in an automated optimal design procedure is challenging. The proposed approach, based on a well-known and established optimization algorithm, but exploiting a new concept of degree of conflict, can lead to new results in the field of automated optimal design in a many-objective context. |
doi_str_mv | 10.1108/COMPEL-10-2021-0394 |
format | article |
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This paper aims to deal with the optimal shape design of a class of permanent magnet motors by minimizing multiple objectives according to an original interpretation of Pareto optimality. The proposed method solves a many-objective problems characterized by five objective functions and five design variables with evolution strategy algorithms, classically used for single- and multi-objective (two objective functions) optimization problems.
Design/methodology/approach
Two approaches are proposed in the paper: the All-Objectives (AO) and the Many-Objectives (MO) optimization approach. The former is based on a single-objective optimization of a preference function, i.e. a normalized weighted sum. In contrast, in the MO a multi-objective optimization algorithm is applied to the minimization of a weight-free preference function and simultaneously to a maximization of the distance of the current solution from the prototype. The optimizations are based on an equivalent circuit model of the Permanent Magnet (PM) motor, but the results are assessed by means of finite element analyses (FEAs).
Findings
An extensive study of the solutions obtained by means of the different optimization approaches is provided by means of post-processing analyses. Both the approaches find non-dominated solutions with respect to the prototype that are substantially improving the initial solution. The points of strength along with the weakness points of each solution with respect to the prototype are analysed in depth.
Practical implications
The paper gives a good guide to the designers of electric motors, focussed on a shape design optimization.
Originality/value
Considering simultaneously five objective functions in an automated optimal design procedure is challenging. The proposed approach, based on a well-known and established optimization algorithm, but exploiting a new concept of degree of conflict, can lead to new results in the field of automated optimal design in a many-objective context.</description><identifier>ISSN: 0332-1649</identifier><identifier>EISSN: 2054-5606</identifier><identifier>EISSN: 0332-1649</identifier><identifier>DOI: 10.1108/COMPEL-10-2021-0394</identifier><language>eng</language><publisher>Bradford: Emerald Publishing Limited</publisher><subject>Case studies ; Context ; Design optimization ; Electric motors ; Energy efficiency ; Equivalent circuits ; Evolutionary algorithms ; Finite element method ; Geometry ; Magnetism ; Multiple objective analysis ; Optimization techniques ; Pareto optimization ; Permanent magnets ; Prototypes</subject><ispartof>Compel, 2022-10, Vol.41 (6), p.1994-2009</ispartof><rights>Emerald Publishing Limited</rights><rights>Emerald Publishing Limited.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c320t-17ff62132fb30bfc68bf1feffcc8695eaa791ad9028711d016a37e68d9277c093</citedby><cites>FETCH-LOGICAL-c320t-17ff62132fb30bfc68bf1feffcc8695eaa791ad9028711d016a37e68d9277c093</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2719317427/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2719317427?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,11688,27924,27925,36060,44363,74895</link.rule.ids></links><search><creatorcontrib>Di Barba, Paolo</creatorcontrib><creatorcontrib>Mognaschi, Maria Evelina</creatorcontrib><creatorcontrib>Petkovska, Lidija</creatorcontrib><creatorcontrib>Cvetkovski, Goga Vladimir</creatorcontrib><title>Optimal shape design of a class of permanent magnet motors in a multiple-objectives context</title><title>Compel</title><description>Purpose
This paper aims to deal with the optimal shape design of a class of permanent magnet motors by minimizing multiple objectives according to an original interpretation of Pareto optimality. The proposed method solves a many-objective problems characterized by five objective functions and five design variables with evolution strategy algorithms, classically used for single- and multi-objective (two objective functions) optimization problems.
Design/methodology/approach
Two approaches are proposed in the paper: the All-Objectives (AO) and the Many-Objectives (MO) optimization approach. The former is based on a single-objective optimization of a preference function, i.e. a normalized weighted sum. In contrast, in the MO a multi-objective optimization algorithm is applied to the minimization of a weight-free preference function and simultaneously to a maximization of the distance of the current solution from the prototype. The optimizations are based on an equivalent circuit model of the Permanent Magnet (PM) motor, but the results are assessed by means of finite element analyses (FEAs).
Findings
An extensive study of the solutions obtained by means of the different optimization approaches is provided by means of post-processing analyses. Both the approaches find non-dominated solutions with respect to the prototype that are substantially improving the initial solution. The points of strength along with the weakness points of each solution with respect to the prototype are analysed in depth.
Practical implications
The paper gives a good guide to the designers of electric motors, focussed on a shape design optimization.
Originality/value
Considering simultaneously five objective functions in an automated optimal design procedure is challenging. The proposed approach, based on a well-known and established optimization algorithm, but exploiting a new concept of degree of conflict, can lead to new results in the field of automated optimal design in a many-objective context.</description><subject>Case studies</subject><subject>Context</subject><subject>Design optimization</subject><subject>Electric motors</subject><subject>Energy efficiency</subject><subject>Equivalent circuits</subject><subject>Evolutionary algorithms</subject><subject>Finite element method</subject><subject>Geometry</subject><subject>Magnetism</subject><subject>Multiple objective analysis</subject><subject>Optimization techniques</subject><subject>Pareto optimization</subject><subject>Permanent magnets</subject><subject>Prototypes</subject><issn>0332-1649</issn><issn>2054-5606</issn><issn>0332-1649</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>M0C</sourceid><recordid>eNp1kD1PwzAQhi0EEqXwC1gsMRv8kdjxiKryIRWVASaGyHHOJVUSB9tF8O9JFBYGbnnvpPe90z0IXTJ6zRgtblbbp-f1hjBKOOWMUKGzI7TgNM9ILqk8RgsqBCdMZvoUncW4p2PpnC7Q23ZITWdaHN_NALiG2Ox67B022LYmxqkdIHSmhz7hzux6GMUnHyJu-tHVHdrUDC0QX-3BpuYTIra-T_CVztGJM22Ei19dote79cvqgWy294-r2w2xgtNEmHJOcia4qwStnJVF5ZgD56wtpM7BGKWZqTXlhWKspkwaoUAWteZKWarFEl3Ne4fgPw4QU7n3h9CPJ0uumBZMZVyNLjG7bPAxBnDlEMbPw3fJaDlRLGeK0zhRLCeKY4rPKeggmLb-J_QHvfgBw5h1Dg</recordid><startdate>20221003</startdate><enddate>20221003</enddate><creator>Di Barba, Paolo</creator><creator>Mognaschi, Maria Evelina</creator><creator>Petkovska, Lidija</creator><creator>Cvetkovski, Goga Vladimir</creator><general>Emerald Publishing Limited</general><general>Emerald Group Publishing Limited</general><scope>AAYXX</scope><scope>CITATION</scope><scope>0U~</scope><scope>1-H</scope><scope>7SC</scope><scope>7SP</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</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>F~G</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K6~</scope><scope>K7-</scope><scope>L.-</scope><scope>L.0</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0C</scope><scope>M0N</scope><scope>M2P</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PQBIZ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYYUZ</scope><scope>Q9U</scope></search><sort><creationdate>20221003</creationdate><title>Optimal shape design of a class of permanent magnet motors in a multiple-objectives context</title><author>Di Barba, Paolo ; Mognaschi, Maria Evelina ; Petkovska, Lidija ; Cvetkovski, Goga Vladimir</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c320t-17ff62132fb30bfc68bf1feffcc8695eaa791ad9028711d016a37e68d9277c093</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Case studies</topic><topic>Context</topic><topic>Design optimization</topic><topic>Electric motors</topic><topic>Energy efficiency</topic><topic>Equivalent circuits</topic><topic>Evolutionary algorithms</topic><topic>Finite element method</topic><topic>Geometry</topic><topic>Magnetism</topic><topic>Multiple objective analysis</topic><topic>Optimization techniques</topic><topic>Pareto optimization</topic><topic>Permanent magnets</topic><topic>Prototypes</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Di Barba, Paolo</creatorcontrib><creatorcontrib>Mognaschi, Maria Evelina</creatorcontrib><creatorcontrib>Petkovska, Lidija</creatorcontrib><creatorcontrib>Cvetkovski, Goga Vladimir</creatorcontrib><collection>CrossRef</collection><collection>Global News & ABI/Inform Professional</collection><collection>Trade PRO</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>ProQuest Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Business Collection</collection><collection>Computer science database</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Professional Standard</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>Science Database</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>One Business (ProQuest)</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>ABI/INFORM Collection China</collection><collection>ProQuest Central Basic</collection><jtitle>Compel</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Di Barba, Paolo</au><au>Mognaschi, Maria Evelina</au><au>Petkovska, Lidija</au><au>Cvetkovski, Goga Vladimir</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Optimal shape design of a class of permanent magnet motors in a multiple-objectives context</atitle><jtitle>Compel</jtitle><date>2022-10-03</date><risdate>2022</risdate><volume>41</volume><issue>6</issue><spage>1994</spage><epage>2009</epage><pages>1994-2009</pages><issn>0332-1649</issn><eissn>2054-5606</eissn><eissn>0332-1649</eissn><abstract>Purpose
This paper aims to deal with the optimal shape design of a class of permanent magnet motors by minimizing multiple objectives according to an original interpretation of Pareto optimality. The proposed method solves a many-objective problems characterized by five objective functions and five design variables with evolution strategy algorithms, classically used for single- and multi-objective (two objective functions) optimization problems.
Design/methodology/approach
Two approaches are proposed in the paper: the All-Objectives (AO) and the Many-Objectives (MO) optimization approach. The former is based on a single-objective optimization of a preference function, i.e. a normalized weighted sum. In contrast, in the MO a multi-objective optimization algorithm is applied to the minimization of a weight-free preference function and simultaneously to a maximization of the distance of the current solution from the prototype. The optimizations are based on an equivalent circuit model of the Permanent Magnet (PM) motor, but the results are assessed by means of finite element analyses (FEAs).
Findings
An extensive study of the solutions obtained by means of the different optimization approaches is provided by means of post-processing analyses. Both the approaches find non-dominated solutions with respect to the prototype that are substantially improving the initial solution. The points of strength along with the weakness points of each solution with respect to the prototype are analysed in depth.
Practical implications
The paper gives a good guide to the designers of electric motors, focussed on a shape design optimization.
Originality/value
Considering simultaneously five objective functions in an automated optimal design procedure is challenging. The proposed approach, based on a well-known and established optimization algorithm, but exploiting a new concept of degree of conflict, can lead to new results in the field of automated optimal design in a many-objective context.</abstract><cop>Bradford</cop><pub>Emerald Publishing Limited</pub><doi>10.1108/COMPEL-10-2021-0394</doi><tpages>16</tpages></addata></record> |
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source | ABI/INFORM Global; Emerald:Jisc Collections:Emerald Subject Collections HE and FE 2024-2026:Emerald Premier (reading list) |
subjects | Case studies Context Design optimization Electric motors Energy efficiency Equivalent circuits Evolutionary algorithms Finite element method Geometry Magnetism Multiple objective analysis Optimization techniques Pareto optimization Permanent magnets Prototypes |
title | Optimal shape design of a class of permanent magnet motors in a multiple-objectives context |
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