Multi-objective mean particle swarm optimization algorithm
In this paper, Pareto non-dominated ranking, crowding distance, tournament selection methods and mean particle swarm optimization were introduced, we using these concepts, a novel mean particle swarm optimization algorithm for multi-objective optimization problem is proposed. Finally, three standard...
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| creator | Shengyu Pei Yongquan Zhou |
| description | In this paper, Pareto non-dominated ranking, crowding distance, tournament selection methods and mean particle swarm optimization were introduced, we using these concepts, a novel mean particle swarm optimization algorithm for multi-objective optimization problem is proposed. Finally, three standard non-constrained multi-objective functions and four constrained multi-objective functions are used to test the performance of the algorithm. The experiment results show that the proposed approach is an efficient and feasible. |
| doi_str_mv | 10.1109/WCICA.2010.5553900 |
| format | Conference Proceeding |
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Finally, three standard non-constrained multi-objective functions and four constrained multi-objective functions are used to test the performance of the algorithm. 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The experiment results show that the proposed approach is an efficient and feasible.</description><subject>Algorithm design and analysis</subject><subject>Biological system modeling</subject><subject>Computers</subject><subject>Crowding distance</subject><subject>Mean particle swarm optimization</subject><subject>Multi-objective constrained optimization</subject><subject>Optimization</subject><subject>Pareto non-dominated</subject><subject>Particle swarm optimization</subject><subject>Proposals</subject><isbn>9781424467129</isbn><isbn>142446711X</isbn><isbn>9781424467112</isbn><isbn>1424467128</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj81KxDAURuNCUMa-gG76Ah1z0_w07qT4ByNuFJfDbXqjGZppSaOiT29hZvVxNofzMXYJfA3A7fV7-9TergVfWClVW85PWGFNA1JIqQ0Ie8aKed5xzsFoLbQ4ZzfPX0MO1djtyOXwTWUk3JcTphzcQOX8gymW45RDDH-Yw7gvcfgYU8if8YKdehxmKo67Ym_3d6_tY7V5eVhCNlUAo3Ile4dOe2xQCF9z3aN1Hp3Q6MF0zhqhjFxyyHljLGgE0fNGdc7JHrymesWuDt5ARNsphYjpd3t8WP8DhStH9w</recordid><startdate>201007</startdate><enddate>201007</enddate><creator>Shengyu Pei</creator><creator>Yongquan Zhou</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201007</creationdate><title>Multi-objective mean particle swarm optimization algorithm</title><author>Shengyu Pei ; Yongquan Zhou</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-4dcac6fa8a22f306da9cfac26af17bc972574766ecf77916a12d085bcc4d1f6e3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>chi ; eng</language><creationdate>2010</creationdate><topic>Algorithm design and analysis</topic><topic>Biological system modeling</topic><topic>Computers</topic><topic>Crowding distance</topic><topic>Mean particle swarm optimization</topic><topic>Multi-objective constrained optimization</topic><topic>Optimization</topic><topic>Pareto non-dominated</topic><topic>Particle swarm optimization</topic><topic>Proposals</topic><toplevel>online_resources</toplevel><creatorcontrib>Shengyu Pei</creatorcontrib><creatorcontrib>Yongquan Zhou</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library Online</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Shengyu Pei</au><au>Yongquan Zhou</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Multi-objective mean particle swarm optimization algorithm</atitle><btitle>2010 8th World Congress on Intelligent Control and Automation</btitle><stitle>WCICA</stitle><date>2010-07</date><risdate>2010</risdate><spage>3315</spage><epage>3319</epage><pages>3315-3319</pages><eisbn>9781424467129</eisbn><eisbn>142446711X</eisbn><eisbn>9781424467112</eisbn><eisbn>1424467128</eisbn><abstract>In this paper, Pareto non-dominated ranking, crowding distance, tournament selection methods and mean particle swarm optimization were introduced, we using these concepts, a novel mean particle swarm optimization algorithm for multi-objective optimization problem is proposed. Finally, three standard non-constrained multi-objective functions and four constrained multi-objective functions are used to test the performance of the algorithm. The experiment results show that the proposed approach is an efficient and feasible.</abstract><pub>IEEE</pub><doi>10.1109/WCICA.2010.5553900</doi><tpages>5</tpages></addata></record> |
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| language | chi ; eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Algorithm design and analysis Biological system modeling Computers Crowding distance Mean particle swarm optimization Multi-objective constrained optimization Optimization Pareto non-dominated Particle swarm optimization Proposals |
| title | Multi-objective mean particle swarm optimization algorithm |
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