A Visual Contrast–Based Fruit Fly Algorithm for Optimizing Conventional and Nonconventional Machining Processes
Swarm intelligence has been extensively adopted to develop and deploy optimization algorithms to almost all branches of science and engineering. In this paper, a visual contrast–based fruit fly algorithm ( c-mFOA ) is presented to push further the improvement of intelligent optimization when it come...
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| Veröffentlicht in: | International journal of advanced manufacturing technology 2020-08, Vol.109 (9-12), p.2901-2914 |
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| container_title | International journal of advanced manufacturing technology |
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| creator | Fountas, Nikolaos A. Kanarachos, Stratis Stergiou, Constantinos I. |
| description | Swarm intelligence has been extensively adopted to develop and deploy optimization algorithms to almost all branches of science and engineering. In this paper, a visual contrast–based fruit fly algorithm (
c-mFOA
) is presented to push further the improvement of intelligent optimization when it comes to general engineering problem solving with emphasis to conventional and nonconventional manufacturing processes implemented to modern industry. In this fruit fly algorithmic variant, the natural mechanisms of surging, visual contrast, and casting are incorporated to enhance the algorithm’s exploration and exploitation. The proposed algorithm has been tested to optimize a set of known, widely used benchmark functions and is further implemented to optimize the process parameters of machining processes namely turning; focused ion beam micro milling; laser cutting; wire electrodischarge machining; and microwire electrodischarge machining. The results obtained by examining the multiple solutions, their nonparametric statistical outputs, and hypervolumes of their related Pareto fronts, suggest clear superiority of the
c-mFOA
against its competing multiobjective optimization algorithms (MOEAs). |
| doi_str_mv | 10.1007/s00170-020-05841-6 |
| format | Article |
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c-mFOA
) is presented to push further the improvement of intelligent optimization when it comes to general engineering problem solving with emphasis to conventional and nonconventional manufacturing processes implemented to modern industry. In this fruit fly algorithmic variant, the natural mechanisms of surging, visual contrast, and casting are incorporated to enhance the algorithm’s exploration and exploitation. The proposed algorithm has been tested to optimize a set of known, widely used benchmark functions and is further implemented to optimize the process parameters of machining processes namely turning; focused ion beam micro milling; laser cutting; wire electrodischarge machining; and microwire electrodischarge machining. The results obtained by examining the multiple solutions, their nonparametric statistical outputs, and hypervolumes of their related Pareto fronts, suggest clear superiority of the
c-mFOA
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c-mFOA
) is presented to push further the improvement of intelligent optimization when it comes to general engineering problem solving with emphasis to conventional and nonconventional manufacturing processes implemented to modern industry. In this fruit fly algorithmic variant, the natural mechanisms of surging, visual contrast, and casting are incorporated to enhance the algorithm’s exploration and exploitation. The proposed algorithm has been tested to optimize a set of known, widely used benchmark functions and is further implemented to optimize the process parameters of machining processes namely turning; focused ion beam micro milling; laser cutting; wire electrodischarge machining; and microwire electrodischarge machining. The results obtained by examining the multiple solutions, their nonparametric statistical outputs, and hypervolumes of their related Pareto fronts, suggest clear superiority of the
c-mFOA
against its competing multiobjective optimization algorithms (MOEAs).</description><subject>Algorithms</subject><subject>CAE) and Design</subject><subject>Computer-Aided Engineering (CAD</subject><subject>Electric discharge machining</subject><subject>Engineering</subject><subject>Fruit flies</subject><subject>Industrial and Production Engineering</subject><subject>Ion beams</subject><subject>Laser beam cutting</subject><subject>Mechanical Engineering</subject><subject>Media Management</subject><subject>Milling (machining)</subject><subject>Multiple objective analysis</subject><subject>Optimization algorithms</subject><subject>Original Article</subject><subject>Pareto optimization</subject><subject>Process parameters</subject><subject>Swarm intelligence</subject><subject>Turning (machining)</subject><subject>Visual flight</subject><issn>0268-3768</issn><issn>1433-3015</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp9kMtOwzAQRS0EEqXwA6wssQ74ETvOslQUkMpjAWwtJ3FaV6nd2g5SWfEP_CFfQkKQYMViNNLo3KvRAeAUo3OMUHYREMIZShDphokUJ3wPjHBKaUIRZvtghAgXCc24OARHIaw6nGMuRmA7gS8mtKqBU2ejVyF-vn9cqqArOPOtiXDW7OCkWThv4nINa-fhwyaatXkzdtFnXrWNxtmuQNkK3jtb_r3dqXJpbI8-elfqEHQ4Bge1aoI--dlj8Dy7epreJPOH69vpZJ6UlGcxqYlgrGCM4yJlmUaiEiyvSUlSzPMCC1URVmcKKYUzTPt7rXNUpAhRLIqc0TE4G3o33m1bHaJcudZ3TwVJ0hyJlBDaU2SgSu9C8LqWG2_Wyu8kRrJXKwe1slMrv9VK3oXoEAodbBfa_1b_k_oC2RF9PA</recordid><startdate>20200801</startdate><enddate>20200801</enddate><creator>Fountas, Nikolaos A.</creator><creator>Kanarachos, Stratis</creator><creator>Stergiou, Constantinos I.</creator><general>Springer London</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><orcidid>https://orcid.org/0000-0001-5859-6503</orcidid></search><sort><creationdate>20200801</creationdate><title>A Visual Contrast–Based Fruit Fly Algorithm for Optimizing Conventional and Nonconventional Machining Processes</title><author>Fountas, Nikolaos A. ; Kanarachos, Stratis ; Stergiou, Constantinos I.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c367t-f2855b5561b457e08d859f2c24169b18ad25f7a0aa17132c24fe90b400318b953</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>CAE) and Design</topic><topic>Computer-Aided Engineering (CAD</topic><topic>Electric discharge machining</topic><topic>Engineering</topic><topic>Fruit flies</topic><topic>Industrial and Production Engineering</topic><topic>Ion beams</topic><topic>Laser beam cutting</topic><topic>Mechanical Engineering</topic><topic>Media Management</topic><topic>Milling (machining)</topic><topic>Multiple objective analysis</topic><topic>Optimization algorithms</topic><topic>Original Article</topic><topic>Pareto optimization</topic><topic>Process parameters</topic><topic>Swarm intelligence</topic><topic>Turning (machining)</topic><topic>Visual flight</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fountas, Nikolaos A.</creatorcontrib><creatorcontrib>Kanarachos, Stratis</creatorcontrib><creatorcontrib>Stergiou, Constantinos I.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>Technology Collection (ProQuest)</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>International journal of advanced manufacturing technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fountas, Nikolaos A.</au><au>Kanarachos, Stratis</au><au>Stergiou, Constantinos I.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Visual Contrast–Based Fruit Fly Algorithm for Optimizing Conventional and Nonconventional Machining Processes</atitle><jtitle>International journal of advanced manufacturing technology</jtitle><stitle>Int J Adv Manuf Technol</stitle><date>2020-08-01</date><risdate>2020</risdate><volume>109</volume><issue>9-12</issue><spage>2901</spage><epage>2914</epage><pages>2901-2914</pages><issn>0268-3768</issn><eissn>1433-3015</eissn><abstract>Swarm intelligence has been extensively adopted to develop and deploy optimization algorithms to almost all branches of science and engineering. In this paper, a visual contrast–based fruit fly algorithm (
c-mFOA
) is presented to push further the improvement of intelligent optimization when it comes to general engineering problem solving with emphasis to conventional and nonconventional manufacturing processes implemented to modern industry. In this fruit fly algorithmic variant, the natural mechanisms of surging, visual contrast, and casting are incorporated to enhance the algorithm’s exploration and exploitation. The proposed algorithm has been tested to optimize a set of known, widely used benchmark functions and is further implemented to optimize the process parameters of machining processes namely turning; focused ion beam micro milling; laser cutting; wire electrodischarge machining; and microwire electrodischarge machining. The results obtained by examining the multiple solutions, their nonparametric statistical outputs, and hypervolumes of their related Pareto fronts, suggest clear superiority of the
c-mFOA
against its competing multiobjective optimization algorithms (MOEAs).</abstract><cop>London</cop><pub>Springer London</pub><doi>10.1007/s00170-020-05841-6</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0001-5859-6503</orcidid></addata></record> |
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| ispartof | International journal of advanced manufacturing technology, 2020-08, Vol.109 (9-12), p.2901-2914 |
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| subjects | Algorithms CAE) and Design Computer-Aided Engineering (CAD Electric discharge machining Engineering Fruit flies Industrial and Production Engineering Ion beams Laser beam cutting Mechanical Engineering Media Management Milling (machining) Multiple objective analysis Optimization algorithms Original Article Pareto optimization Process parameters Swarm intelligence Turning (machining) Visual flight |
| title | A Visual Contrast–Based Fruit Fly Algorithm for Optimizing Conventional and Nonconventional Machining Processes |
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