Vortex Particle Swarm Optimization
This paper presents an optimization algorithm based on self-propelled particle swarms which exploit vorticity features in order to avoid local minima; the proposed algorithm is termed Vortex Particle Swarm Optimization (VPSO). The optimization algorithm switches between translational and dispersion...
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| creator | Espitia, Helbert Eduardo Sofrony, Jorge Ivan |
| description | This paper presents an optimization algorithm based on self-propelled particle swarms which exploit vorticity features in order to avoid local minima; the proposed algorithm is termed Vortex Particle Swarm Optimization (VPSO). The optimization algorithm switches between translational and dispersion behavior of the swarm to enhance the exploration of the search space and to avoid getting trapped in local minima. These two types of behavior are induced by choosing the swarm as a collection of coupled, second-order oscillators where it is possible, via suitable parameter selection to switch between translational (convergence) and vortex-like movements (dispersion). This idea mimics living organism strategies such as foraging and predator avoidance. Performance of the algorithm is studied via simulation results of well-known 2D test functions. |
| doi_str_mv | 10.1109/CEC.2013.6557803 |
| format | Conference Proceeding |
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The optimization algorithm switches between translational and dispersion behavior of the swarm to enhance the exploration of the search space and to avoid getting trapped in local minima. These two types of behavior are induced by choosing the swarm as a collection of coupled, second-order oscillators where it is possible, via suitable parameter selection to switch between translational (convergence) and vortex-like movements (dispersion). This idea mimics living organism strategies such as foraging and predator avoidance. 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Performance of the algorithm is studied via simulation results of well-known 2D test functions.</description><subject>Bio-inspired optimization</subject><subject>Dispersion</subject><subject>Equations</subject><subject>Force</subject><subject>Linear programming</subject><subject>Mathematical model</subject><subject>Optimization</subject><subject>Particle swarm optimization</subject><subject>PSO</subject><subject>vortex behavior</subject><issn>1089-778X</issn><issn>1941-0026</issn><isbn>1479904538</isbn><isbn>9781479904532</isbn><isbn>147990452X</isbn><isbn>9781479904525</isbn><isbn>9781479904549</isbn><isbn>1479904546</isbn><isbn>9781479904518</isbn><isbn>1479904511</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2013</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFj8tKAzEUQOMLrLV7wc3gPuO9uckkWcpQbaFQwQfdlZuQgUjHlpkBH1_fggVXZ3HgwBHiBqFEBH9fT-tSAVJZGWMd0Im4Qm29B23U6lSM0GuUAKo6-xfkzg8CnJfWutWlmPT9BwAcehaARuLufdsN6bt45m7IcZOKly_u2mK5G3Kbf3nI289rcdHwpk-TI8fi7XH6Ws_kYvk0rx8WMqM1g2QmHxIpJBct6CpBgEgmRMNoqyZop42BoLR1NkRkdIGNYeWb2GjfEI3F7V83p5TWuy633P2sj6-0ByooQj8</recordid><startdate>201306</startdate><enddate>201306</enddate><creator>Espitia, Helbert Eduardo</creator><creator>Sofrony, Jorge Ivan</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201306</creationdate><title>Vortex Particle Swarm Optimization</title><author>Espitia, Helbert Eduardo ; Sofrony, Jorge Ivan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-aa39be32138c7046e0b0c35bc5a176fb484550b24787bc1a18ba55a29fcf49f33</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Bio-inspired optimization</topic><topic>Dispersion</topic><topic>Equations</topic><topic>Force</topic><topic>Linear programming</topic><topic>Mathematical model</topic><topic>Optimization</topic><topic>Particle swarm optimization</topic><topic>PSO</topic><topic>vortex behavior</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Espitia, Helbert Eduardo</creatorcontrib><creatorcontrib>Sofrony, Jorge Ivan</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/IET Electronic Library (IEL)</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>Espitia, Helbert Eduardo</au><au>Sofrony, Jorge Ivan</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Vortex Particle Swarm Optimization</atitle><btitle>2013 IEEE Congress on Evolutionary Computation</btitle><stitle>CEC</stitle><date>2013-06</date><risdate>2013</risdate><spage>1992</spage><epage>1998</epage><pages>1992-1998</pages><issn>1089-778X</issn><eissn>1941-0026</eissn><isbn>1479904538</isbn><isbn>9781479904532</isbn><eisbn>147990452X</eisbn><eisbn>9781479904525</eisbn><eisbn>9781479904549</eisbn><eisbn>1479904546</eisbn><eisbn>9781479904518</eisbn><eisbn>1479904511</eisbn><abstract>This paper presents an optimization algorithm based on self-propelled particle swarms which exploit vorticity features in order to avoid local minima; the proposed algorithm is termed Vortex Particle Swarm Optimization (VPSO). The optimization algorithm switches between translational and dispersion behavior of the swarm to enhance the exploration of the search space and to avoid getting trapped in local minima. These two types of behavior are induced by choosing the swarm as a collection of coupled, second-order oscillators where it is possible, via suitable parameter selection to switch between translational (convergence) and vortex-like movements (dispersion). This idea mimics living organism strategies such as foraging and predator avoidance. Performance of the algorithm is studied via simulation results of well-known 2D test functions.</abstract><pub>IEEE</pub><doi>10.1109/CEC.2013.6557803</doi><tpages>7</tpages></addata></record> |
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| ispartof | 2013 IEEE Congress on Evolutionary Computation, 2013, p.1992-1998 |
| issn | 1089-778X 1941-0026 |
| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Bio-inspired optimization Dispersion Equations Force Linear programming Mathematical model Optimization Particle swarm optimization PSO vortex behavior |
| title | Vortex Particle Swarm Optimization |
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