A real application example of a control structure selection by means of a multiobjective genetic algorithm
Control problems are clear examples of multiobjective optimization. In this kind of problems a series of objectives, some of them opposed to each other, will be optimized in order to fit some design specifications. Moreover, evolutionary algorithms have been shown to be ideal for the resolution of t...
Gespeichert in:
| Veröffentlicht in: | Artificial Neural Nets Problem Solving Methods 2003, p.369-376 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 376 |
|---|---|
| container_issue | |
| container_start_page | 369 |
| container_title | Artificial Neural Nets Problem Solving Methods |
| container_volume | |
| creator | Sánchez, M. Parrilla Almansa, J. Aranda |
| description | Control problems are clear examples of multiobjective optimization. In this kind of problems a series of objectives, some of them opposed to each other, will be optimized in order to fit some design specifications.
Moreover, evolutionary algorithms have been shown to be ideal for the resolution of these kinds of problems because they work simultaneously with a set of possible solutions, thereby favoring convergence towards a global optimum. In this document we propose a way of dealing with the different objectives considered and a genetic-evolutionary algorithm that will enable some phases of the controller design to be automated.
Finally, an application example of the methods outlined will be applied to the design of a controller to reduce the sickness index on a high-speed ship. |
| doi_str_mv | 10.1007/3-540-44869-1_47 |
| format | Article |
| fullrecord | <record><control><sourceid>pascalfrancis_sprin</sourceid><recordid>TN_cdi_pascalfrancis_primary_15670995</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>15670995</sourcerecordid><originalsourceid>FETCH-LOGICAL-g277t-ea36036f5045027974a493506edb5166f4919407f1e4c9ca6a62b6290fb0ddf13</originalsourceid><addsrcrecordid>eNpFkE1LAzEQhuMXWGrvHnPxmDrZZJPmWIpfUPCi5zCbJnVr9oNkK_rv3baCcxl434dheAi55TDnAPpesFICk3KhDONW6jMyM3ohxvCYiXMy4YpzJoQ0F_8dFJzLSzIBAQUzWoprMst5B-NIU3C5mJDdkiaPkWLfx9rhUHct9d_Y9NHTLlCkrmuH1EWah7R3wz55mn307ghWP7Tx2OYT2ezjmFa7Q_nl6da3fqgdxbjtUj18NDfkKmDMfva3p-T98eFt9czWr08vq-WabQutB-ZRKBAqlCBLKPT4NkojSlB-U5VcqSANNxJ04F4641ChKipVGAgVbDaBiym5O93tMTuMIWHr6mz7VDeYfiwvlQZjypGbn7g8Vu3WJ1t13We2HOxBuhV2dGiPgu1BuvgFaPNvqw</addsrcrecordid><sourcetype>Index Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>A real application example of a control structure selection by means of a multiobjective genetic algorithm</title><source>Springer Books</source><creator>Sánchez, M. Parrilla ; Almansa, J. Aranda</creator><contributor>Mira, José ; Álvarez, José R.</contributor><creatorcontrib>Sánchez, M. Parrilla ; Almansa, J. Aranda ; Mira, José ; Álvarez, José R.</creatorcontrib><description>Control problems are clear examples of multiobjective optimization. In this kind of problems a series of objectives, some of them opposed to each other, will be optimized in order to fit some design specifications.
Moreover, evolutionary algorithms have been shown to be ideal for the resolution of these kinds of problems because they work simultaneously with a set of possible solutions, thereby favoring convergence towards a global optimum. In this document we propose a way of dealing with the different objectives considered and a genetic-evolutionary algorithm that will enable some phases of the controller design to be automated.
Finally, an application example of the methods outlined will be applied to the design of a controller to reduce the sickness index on a high-speed ship.</description><identifier>ISSN: 0302-9743</identifier><identifier>ISBN: 9783540402114</identifier><identifier>ISBN: 354040211X</identifier><identifier>EISSN: 1611-3349</identifier><identifier>EISBN: 9783540448693</identifier><identifier>EISBN: 3540448691</identifier><identifier>DOI: 10.1007/3-540-44869-1_47</identifier><language>eng</language><publisher>Berlin, Heidelberg: Springer Berlin Heidelberg</publisher><subject>Applied sciences ; Computer science; control theory; systems ; Exact sciences and technology ; Software</subject><ispartof>Artificial Neural Nets Problem Solving Methods, 2003, p.369-376</ispartof><rights>Springer-Verlag Berlin Heidelberg 2003</rights><rights>2004 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/3-540-44869-1_47$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/3-540-44869-1_47$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>309,310,775,776,780,785,786,789,4035,4036,27904,38234,41421,42490</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=15670995$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><contributor>Mira, José</contributor><contributor>Álvarez, José R.</contributor><creatorcontrib>Sánchez, M. Parrilla</creatorcontrib><creatorcontrib>Almansa, J. Aranda</creatorcontrib><title>A real application example of a control structure selection by means of a multiobjective genetic algorithm</title><title>Artificial Neural Nets Problem Solving Methods</title><description>Control problems are clear examples of multiobjective optimization. In this kind of problems a series of objectives, some of them opposed to each other, will be optimized in order to fit some design specifications.
Moreover, evolutionary algorithms have been shown to be ideal for the resolution of these kinds of problems because they work simultaneously with a set of possible solutions, thereby favoring convergence towards a global optimum. In this document we propose a way of dealing with the different objectives considered and a genetic-evolutionary algorithm that will enable some phases of the controller design to be automated.
Finally, an application example of the methods outlined will be applied to the design of a controller to reduce the sickness index on a high-speed ship.</description><subject>Applied sciences</subject><subject>Computer science; control theory; systems</subject><subject>Exact sciences and technology</subject><subject>Software</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>9783540402114</isbn><isbn>354040211X</isbn><isbn>9783540448693</isbn><isbn>3540448691</isbn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2003</creationdate><recordtype>article</recordtype><recordid>eNpFkE1LAzEQhuMXWGrvHnPxmDrZZJPmWIpfUPCi5zCbJnVr9oNkK_rv3baCcxl434dheAi55TDnAPpesFICk3KhDONW6jMyM3ohxvCYiXMy4YpzJoQ0F_8dFJzLSzIBAQUzWoprMst5B-NIU3C5mJDdkiaPkWLfx9rhUHct9d_Y9NHTLlCkrmuH1EWah7R3wz55mn307ghWP7Tx2OYT2ezjmFa7Q_nl6da3fqgdxbjtUj18NDfkKmDMfva3p-T98eFt9czWr08vq-WabQutB-ZRKBAqlCBLKPT4NkojSlB-U5VcqSANNxJ04F4641ChKipVGAgVbDaBiym5O93tMTuMIWHr6mz7VDeYfiwvlQZjypGbn7g8Vu3WJ1t13We2HOxBuhV2dGiPgu1BuvgFaPNvqw</recordid><startdate>2003</startdate><enddate>2003</enddate><creator>Sánchez, M. Parrilla</creator><creator>Almansa, J. Aranda</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>2003</creationdate><title>A real application example of a control structure selection by means of a multiobjective genetic algorithm</title><author>Sánchez, M. Parrilla ; Almansa, J. Aranda</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-g277t-ea36036f5045027974a493506edb5166f4919407f1e4c9ca6a62b6290fb0ddf13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2003</creationdate><topic>Applied sciences</topic><topic>Computer science; control theory; systems</topic><topic>Exact sciences and technology</topic><topic>Software</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sánchez, M. Parrilla</creatorcontrib><creatorcontrib>Almansa, J. Aranda</creatorcontrib><collection>Pascal-Francis</collection><jtitle>Artificial Neural Nets Problem Solving Methods</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sánchez, M. Parrilla</au><au>Almansa, J. Aranda</au><au>Mira, José</au><au>Álvarez, José R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A real application example of a control structure selection by means of a multiobjective genetic algorithm</atitle><jtitle>Artificial Neural Nets Problem Solving Methods</jtitle><date>2003</date><risdate>2003</risdate><spage>369</spage><epage>376</epage><pages>369-376</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>9783540402114</isbn><isbn>354040211X</isbn><eisbn>9783540448693</eisbn><eisbn>3540448691</eisbn><abstract>Control problems are clear examples of multiobjective optimization. In this kind of problems a series of objectives, some of them opposed to each other, will be optimized in order to fit some design specifications.
Moreover, evolutionary algorithms have been shown to be ideal for the resolution of these kinds of problems because they work simultaneously with a set of possible solutions, thereby favoring convergence towards a global optimum. In this document we propose a way of dealing with the different objectives considered and a genetic-evolutionary algorithm that will enable some phases of the controller design to be automated.
Finally, an application example of the methods outlined will be applied to the design of a controller to reduce the sickness index on a high-speed ship.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/3-540-44869-1_47</doi><tpages>8</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Artificial Neural Nets Problem Solving Methods, 2003, p.369-376 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_15670995 |
| source | Springer Books |
| subjects | Applied sciences Computer science control theory systems Exact sciences and technology Software |
| title | A real application example of a control structure selection by means of a multiobjective genetic algorithm |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-27T05%3A12%3A21IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-pascalfrancis_sprin&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20real%20application%20example%20of%20a%20control%20structure%20selection%20by%20means%20of%20a%20multiobjective%20genetic%20algorithm&rft.jtitle=Artificial%20Neural%20Nets%20Problem%20Solving%20Methods&rft.au=S%C3%A1nchez,%20M.%20Parrilla&rft.date=2003&rft.spage=369&rft.epage=376&rft.pages=369-376&rft.issn=0302-9743&rft.eissn=1611-3349&rft.isbn=9783540402114&rft.isbn_list=354040211X&rft_id=info:doi/10.1007/3-540-44869-1_47&rft_dat=%3Cpascalfrancis_sprin%3E15670995%3C/pascalfrancis_sprin%3E%3Curl%3E%3C/url%3E&rft.eisbn=9783540448693&rft.eisbn_list=3540448691&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |