Multiobjective wing design using genetic algorithms and fuzzy logic
Abstract The designer frequently faces problems in which the project depends on many parameters and the final solution must be evaluated according to several optimization objectives. This may be the case for the aeroelastic and aeromechanical design of lifting surfaces. The aim of the present paper...
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| Veröffentlicht in: | Proceedings of the Institution of Mechanical Engineers. Part G, Journal of aerospace engineering Journal of aerospace engineering, 2004-04, Vol.218 (2), p.133-145 |
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| container_title | Proceedings of the Institution of Mechanical Engineers. Part G, Journal of aerospace engineering |
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| creator | Saggiani, G M Caligiana, G Persiani, F |
| description | Abstract
The designer frequently faces problems in which the project depends on many parameters and the final solution must be evaluated according to several optimization objectives. This may be the case for the aeroelastic and aeromechanical design of lifting surfaces. The aim of the present paper is to show how the combined techniques of genetic algorithms (GAs) and fuzzy logic can be useful in these situations. The leading idea is the development of a tool to assist the designer in the preliminary phase of activity by assigning a genetic code to every possible solution and assessing its performance by means of a fuzzy controller in order to handle simultaneously different design criteria. Examples are given for some typical problems in aeronautical design. |
| doi_str_mv | 10.1243/0954410041321961 |
| format | Article |
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The designer frequently faces problems in which the project depends on many parameters and the final solution must be evaluated according to several optimization objectives. This may be the case for the aeroelastic and aeromechanical design of lifting surfaces. The aim of the present paper is to show how the combined techniques of genetic algorithms (GAs) and fuzzy logic can be useful in these situations. The leading idea is the development of a tool to assist the designer in the preliminary phase of activity by assigning a genetic code to every possible solution and assessing its performance by means of a fuzzy controller in order to handle simultaneously different design criteria. Examples are given for some typical problems in aeronautical design.</description><identifier>ISSN: 0954-4100</identifier><identifier>EISSN: 2041-3025</identifier><identifier>DOI: 10.1243/0954410041321961</identifier><language>eng</language><publisher>London, England: SAGE Publications</publisher><subject>Aerodynamics ; Design engineering ; Design optimization ; Fuzzy logic ; Genetic algorithms ; Heuristic ; Optimization algorithms</subject><ispartof>Proceedings of the Institution of Mechanical Engineers. Part G, Journal of aerospace engineering, 2004-04, Vol.218 (2), p.133-145</ispartof><rights>2004 Institution of Mechanical Engineers</rights><rights>Copyright Mechanical Engineering Publications, Ltd. Apr 2004</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c400t-98e1ee6dbb8394a3e89f5916473ac4ed608641185ddc508c3b210695bf9f73993</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://journals.sagepub.com/doi/pdf/10.1243/0954410041321961$$EPDF$$P50$$Gsage$$H</linktopdf><linktohtml>$$Uhttps://journals.sagepub.com/doi/10.1243/0954410041321961$$EHTML$$P50$$Gsage$$H</linktohtml><link.rule.ids>314,776,780,21798,27901,27902,43597,43598</link.rule.ids></links><search><creatorcontrib>Saggiani, G M</creatorcontrib><creatorcontrib>Caligiana, G</creatorcontrib><creatorcontrib>Persiani, F</creatorcontrib><title>Multiobjective wing design using genetic algorithms and fuzzy logic</title><title>Proceedings of the Institution of Mechanical Engineers. Part G, Journal of aerospace engineering</title><description>Abstract
The designer frequently faces problems in which the project depends on many parameters and the final solution must be evaluated according to several optimization objectives. This may be the case for the aeroelastic and aeromechanical design of lifting surfaces. The aim of the present paper is to show how the combined techniques of genetic algorithms (GAs) and fuzzy logic can be useful in these situations. The leading idea is the development of a tool to assist the designer in the preliminary phase of activity by assigning a genetic code to every possible solution and assessing its performance by means of a fuzzy controller in order to handle simultaneously different design criteria. 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The designer frequently faces problems in which the project depends on many parameters and the final solution must be evaluated according to several optimization objectives. This may be the case for the aeroelastic and aeromechanical design of lifting surfaces. The aim of the present paper is to show how the combined techniques of genetic algorithms (GAs) and fuzzy logic can be useful in these situations. The leading idea is the development of a tool to assist the designer in the preliminary phase of activity by assigning a genetic code to every possible solution and assessing its performance by means of a fuzzy controller in order to handle simultaneously different design criteria. Examples are given for some typical problems in aeronautical design.</abstract><cop>London, England</cop><pub>SAGE Publications</pub><doi>10.1243/0954410041321961</doi><tpages>13</tpages></addata></record> |
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| identifier | ISSN: 0954-4100 |
| ispartof | Proceedings of the Institution of Mechanical Engineers. Part G, Journal of aerospace engineering, 2004-04, Vol.218 (2), p.133-145 |
| issn | 0954-4100 2041-3025 |
| language | eng |
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| source | SAGE Complete |
| subjects | Aerodynamics Design engineering Design optimization Fuzzy logic Genetic algorithms Heuristic Optimization algorithms |
| title | Multiobjective wing design using genetic algorithms and fuzzy logic |
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