Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm
This letter presents a formal stochastic convergence analysis of the standard particle swarm optimization (PSO) algorithm, which involves with randomness. By regarding each particle's position on each evolutionary step as a stochastic vector, the standard PSO algorithm determined by non-negativ...
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| Veröffentlicht in: | Information processing letters 2007-04, Vol.102 (1), p.8-16 |
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| creator | Jiang, M. Luo, Y.P. Yang, S.Y. |
| description | This letter presents a formal stochastic convergence analysis of the standard particle swarm optimization (PSO) algorithm, which involves with randomness. By regarding each particle's position on each evolutionary step as a stochastic vector, the standard PSO algorithm determined by non-negative real parameter tuple
{
ω
,
c
1
,
c
2
}
is analyzed using stochastic process theory. The stochastic convergent condition of the particle swarm system and corresponding parameter selection guidelines are derived. |
| doi_str_mv | 10.1016/j.ipl.2006.10.005 |
| format | Article |
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{
ω
,
c
1
,
c
2
}
is analyzed using stochastic process theory. The stochastic convergent condition of the particle swarm system and corresponding parameter selection guidelines are derived.</description><identifier>ISSN: 0020-0190</identifier><identifier>EISSN: 1872-6119</identifier><identifier>DOI: 10.1016/j.ipl.2006.10.005</identifier><identifier>CODEN: IFPLAT</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Algorithmics. Computability. Computer arithmetics ; Algorithms ; Analysis of algorithms ; Applied sciences ; Computer science; control theory; systems ; Control systems ; Exact sciences and technology ; Mathematics ; Numerical analysis ; Numerical analysis. Scientific computation ; Numerical methods in mathematical programming, optimization and calculus of variations ; Optimization algorithms ; Parameter identification ; Parameter selection ; Particle swarm optimization ; Sciences and techniques of general use ; Stochastic convergence analysis ; Stochastic models ; Stochastic optimization ; Studies ; Theoretical computing</subject><ispartof>Information processing letters, 2007-04, Vol.102 (1), p.8-16</ispartof><rights>2006 Elsevier B.V.</rights><rights>2007 INIST-CNRS</rights><rights>Copyright Elsevier Sequoia S.A. Apr 15, 2007</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c420t-46d28b66fa54e051b366434c67ff515de7de0d335a7616ca99872873d5a8be4f3</citedby><cites>FETCH-LOGICAL-c420t-46d28b66fa54e051b366434c67ff515de7de0d335a7616ca99872873d5a8be4f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.ipl.2006.10.005$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3536,27903,27904,45974</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=18522855$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Jiang, M.</creatorcontrib><creatorcontrib>Luo, Y.P.</creatorcontrib><creatorcontrib>Yang, S.Y.</creatorcontrib><title>Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm</title><title>Information processing letters</title><description>This letter presents a formal stochastic convergence analysis of the standard particle swarm optimization (PSO) algorithm, which involves with randomness. By regarding each particle's position on each evolutionary step as a stochastic vector, the standard PSO algorithm determined by non-negative real parameter tuple
{
ω
,
c
1
,
c
2
}
is analyzed using stochastic process theory. The stochastic convergent condition of the particle swarm system and corresponding parameter selection guidelines are derived.</description><subject>Algorithmics. Computability. Computer arithmetics</subject><subject>Algorithms</subject><subject>Analysis of algorithms</subject><subject>Applied sciences</subject><subject>Computer science; control theory; systems</subject><subject>Control systems</subject><subject>Exact sciences and technology</subject><subject>Mathematics</subject><subject>Numerical analysis</subject><subject>Numerical analysis. Scientific computation</subject><subject>Numerical methods in mathematical programming, optimization and calculus of variations</subject><subject>Optimization algorithms</subject><subject>Parameter identification</subject><subject>Parameter selection</subject><subject>Particle swarm optimization</subject><subject>Sciences and techniques of general use</subject><subject>Stochastic convergence analysis</subject><subject>Stochastic models</subject><subject>Stochastic optimization</subject><subject>Studies</subject><subject>Theoretical computing</subject><issn>0020-0190</issn><issn>1872-6119</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2007</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LxDAQhoMouK7-AG9F8Nh10jZpF0-y-AULHtRzmE2nbpa2qUlc0V9vuit485RkeGYm78PYOYcZBy6vNjMztLMMQMb3DEAcsAmvyiyVnM8P2QQggxT4HI7ZifcbiGCRlxPWPQer1-iD0Ym2_ZbcG_WaEuyx_fLGx0udDOiwo0Au8dSSDsb2iW2SsKbEhwig2zFxRhsrn-i6xA7BdOYbdyy2b9aZsO5O2VGDraez33PKXu9uXxYP6fLp_nFxs0x1kUFIC1ln1UrKBkVBIPgql_G3hZZl0wguaiprgjrPBZaSS43zeUxalXktsFpR0eRTdrGfOzj7_kE-qI39cDGSV1k-ogJ4hPge0s5676hRgzMdui_FQY1S1UZFqWqUOpai1Nhz-TsYvca2cdhr4_8aK5FllRi56z1HMeXWkFNem1FsbVwUqGpr_tnyA4XAjjg</recordid><startdate>20070415</startdate><enddate>20070415</enddate><creator>Jiang, M.</creator><creator>Luo, Y.P.</creator><creator>Yang, S.Y.</creator><general>Elsevier B.V</general><general>Elsevier Science</general><general>Elsevier Sequoia S.A</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20070415</creationdate><title>Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm</title><author>Jiang, M. ; Luo, Y.P. ; Yang, S.Y.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c420t-46d28b66fa54e051b366434c67ff515de7de0d335a7616ca99872873d5a8be4f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Algorithmics. Computability. Computer arithmetics</topic><topic>Algorithms</topic><topic>Analysis of algorithms</topic><topic>Applied sciences</topic><topic>Computer science; control theory; systems</topic><topic>Control systems</topic><topic>Exact sciences and technology</topic><topic>Mathematics</topic><topic>Numerical analysis</topic><topic>Numerical analysis. Scientific computation</topic><topic>Numerical methods in mathematical programming, optimization and calculus of variations</topic><topic>Optimization algorithms</topic><topic>Parameter identification</topic><topic>Parameter selection</topic><topic>Particle swarm optimization</topic><topic>Sciences and techniques of general use</topic><topic>Stochastic convergence analysis</topic><topic>Stochastic models</topic><topic>Stochastic optimization</topic><topic>Studies</topic><topic>Theoretical computing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jiang, M.</creatorcontrib><creatorcontrib>Luo, Y.P.</creatorcontrib><creatorcontrib>Yang, S.Y.</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Information processing letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jiang, M.</au><au>Luo, Y.P.</au><au>Yang, S.Y.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm</atitle><jtitle>Information processing letters</jtitle><date>2007-04-15</date><risdate>2007</risdate><volume>102</volume><issue>1</issue><spage>8</spage><epage>16</epage><pages>8-16</pages><issn>0020-0190</issn><eissn>1872-6119</eissn><coden>IFPLAT</coden><abstract>This letter presents a formal stochastic convergence analysis of the standard particle swarm optimization (PSO) algorithm, which involves with randomness. By regarding each particle's position on each evolutionary step as a stochastic vector, the standard PSO algorithm determined by non-negative real parameter tuple
{
ω
,
c
1
,
c
2
}
is analyzed using stochastic process theory. The stochastic convergent condition of the particle swarm system and corresponding parameter selection guidelines are derived.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.ipl.2006.10.005</doi><tpages>9</tpages></addata></record> |
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| ispartof | Information processing letters, 2007-04, Vol.102 (1), p.8-16 |
| issn | 0020-0190 1872-6119 |
| language | eng |
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| source | Elsevier ScienceDirect Journals |
| subjects | Algorithmics. Computability. Computer arithmetics Algorithms Analysis of algorithms Applied sciences Computer science control theory systems Control systems Exact sciences and technology Mathematics Numerical analysis Numerical analysis. Scientific computation Numerical methods in mathematical programming, optimization and calculus of variations Optimization algorithms Parameter identification Parameter selection Particle swarm optimization Sciences and techniques of general use Stochastic convergence analysis Stochastic models Stochastic optimization Studies Theoretical computing |
| title | Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm |
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