A new hybrid PSO algorithm based on a stochastic Markov chain model
•Development of a new hybrid PSO algorithm.•Parallelism with a Markov chain model.•Testing of the newly developed algorithm on classic benchmark functions. Based on the recent research concerning the PageRank Algorithm used in the famous search engine Google [1], a new Inverse-PageRank-Particle Swar...
Gespeichert in:
| Veröffentlicht in: | Advances in engineering software (1992) 2015-12, Vol.90, p.127-137 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 137 |
|---|---|
| container_issue | |
| container_start_page | 127 |
| container_title | Advances in engineering software (1992) |
| container_volume | 90 |
| creator | Di Cesare, N. Chamoret, D. Domaszewski, M. |
| description | •Development of a new hybrid PSO algorithm.•Parallelism with a Markov chain model.•Testing of the newly developed algorithm on classic benchmark functions.
Based on the recent research concerning the PageRank Algorithm used in the famous search engine Google [1], a new Inverse-PageRank-Particle Swarm Optimizer (I-PR-PSO) is presented in order to improve the performances of classic PSO. The resulted algorithm uses a stochastic Markov chain model to define an intelligent topological structure of the swarm’s population, in which the better particles have an important influence on the others. In the presented experiments, calculations on some benchmark functions classically used to test optimization methods are performed, and the results are compared to different versions of the standard PSO, that is using different topological structures of the population. The experimental results show that I-PR-PSO can converge quicker on the tested functions, and can find better results in the solution domain than its tested peers. |
| doi_str_mv | 10.1016/j.advengsoft.2015.08.005 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_hal_p</sourceid><recordid>TN_cdi_hal_primary_oai_HAL_hal_01202612v1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0965997815001234</els_id><sourcerecordid>1770333194</sourcerecordid><originalsourceid>FETCH-LOGICAL-c435t-819ad8f8c33750ca5a387b1eaa6a5f5702e482bf74946ea498ec78e77e5fcac53</originalsourceid><addsrcrecordid>eNqFkMFOwzAMhnsAiTF4hxzhsOK0TZMexwQMaQgk4Bx5qbtmdM1IuqG9PZ2K4MjJsvX9v-QvihiHmAPPb9YxlntqV8FVXZwAFzGoGECcRCMocjEpCqnOovMQ1gA8g4SPotmUtfTF6sPS25K9vD4zbFbO267esCUGKplrGbLQOVNj6KxhT-g_3J71q23ZxpXUXESnFTaBLn_mOHq_v3ubzSeL54fH2XQxMVkquoniBZaqUiZNpQCDAlMll5wQcxSVkJBQppJlJbMiywmzQpGRiqQkURk0Ih1H10NvjY3eertBf9AOrZ5PF_p4A55AkvNkz3v2amC33n3uKHR6Y4OhpsGW3C5oLiWkacqLrEfVgBrvQvBU_XZz0Eeveq3_vOqjVw1K91776O0Qpf7tvSWvg7HUGiqtJ9Pp0tn_S74BxmqHHw</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1770333194</pqid></control><display><type>article</type><title>A new hybrid PSO algorithm based on a stochastic Markov chain model</title><source>ScienceDirect Journals (5 years ago - present)</source><creator>Di Cesare, N. ; Chamoret, D. ; Domaszewski, M.</creator><creatorcontrib>Di Cesare, N. ; Chamoret, D. ; Domaszewski, M.</creatorcontrib><description>•Development of a new hybrid PSO algorithm.•Parallelism with a Markov chain model.•Testing of the newly developed algorithm on classic benchmark functions.
Based on the recent research concerning the PageRank Algorithm used in the famous search engine Google [1], a new Inverse-PageRank-Particle Swarm Optimizer (I-PR-PSO) is presented in order to improve the performances of classic PSO. The resulted algorithm uses a stochastic Markov chain model to define an intelligent topological structure of the swarm’s population, in which the better particles have an important influence on the others. In the presented experiments, calculations on some benchmark functions classically used to test optimization methods are performed, and the results are compared to different versions of the standard PSO, that is using different topological structures of the population. The experimental results show that I-PR-PSO can converge quicker on the tested functions, and can find better results in the solution domain than its tested peers.</description><identifier>ISSN: 0965-9978</identifier><identifier>DOI: 10.1016/j.advengsoft.2015.08.005</identifier><language>eng</language><publisher>Elsevier Ltd</publisher><subject>Algorithms ; Markov chains ; Materials and structures in mechanics ; Mathematical models ; Mathematics ; Mechanical engineering ; Mechanics ; Mechanics of materials ; Optimization ; Optimization and Control ; PageRank ; Particle Swarm Optimization ; Physics ; Population topology ; Search engines ; Solid mechanics ; Stochasticity ; Structural mechanics ; Topology</subject><ispartof>Advances in engineering software (1992), 2015-12, Vol.90, p.127-137</ispartof><rights>2015 Elsevier Ltd</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c435t-819ad8f8c33750ca5a387b1eaa6a5f5702e482bf74946ea498ec78e77e5fcac53</citedby><cites>FETCH-LOGICAL-c435t-819ad8f8c33750ca5a387b1eaa6a5f5702e482bf74946ea498ec78e77e5fcac53</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.advengsoft.2015.08.005$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>230,314,780,784,885,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttps://hal.science/hal-01202612$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Di Cesare, N.</creatorcontrib><creatorcontrib>Chamoret, D.</creatorcontrib><creatorcontrib>Domaszewski, M.</creatorcontrib><title>A new hybrid PSO algorithm based on a stochastic Markov chain model</title><title>Advances in engineering software (1992)</title><description>•Development of a new hybrid PSO algorithm.•Parallelism with a Markov chain model.•Testing of the newly developed algorithm on classic benchmark functions.
Based on the recent research concerning the PageRank Algorithm used in the famous search engine Google [1], a new Inverse-PageRank-Particle Swarm Optimizer (I-PR-PSO) is presented in order to improve the performances of classic PSO. The resulted algorithm uses a stochastic Markov chain model to define an intelligent topological structure of the swarm’s population, in which the better particles have an important influence on the others. In the presented experiments, calculations on some benchmark functions classically used to test optimization methods are performed, and the results are compared to different versions of the standard PSO, that is using different topological structures of the population. The experimental results show that I-PR-PSO can converge quicker on the tested functions, and can find better results in the solution domain than its tested peers.</description><subject>Algorithms</subject><subject>Markov chains</subject><subject>Materials and structures in mechanics</subject><subject>Mathematical models</subject><subject>Mathematics</subject><subject>Mechanical engineering</subject><subject>Mechanics</subject><subject>Mechanics of materials</subject><subject>Optimization</subject><subject>Optimization and Control</subject><subject>PageRank</subject><subject>Particle Swarm Optimization</subject><subject>Physics</subject><subject>Population topology</subject><subject>Search engines</subject><subject>Solid mechanics</subject><subject>Stochasticity</subject><subject>Structural mechanics</subject><subject>Topology</subject><issn>0965-9978</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><recordid>eNqFkMFOwzAMhnsAiTF4hxzhsOK0TZMexwQMaQgk4Bx5qbtmdM1IuqG9PZ2K4MjJsvX9v-QvihiHmAPPb9YxlntqV8FVXZwAFzGoGECcRCMocjEpCqnOovMQ1gA8g4SPotmUtfTF6sPS25K9vD4zbFbO267esCUGKplrGbLQOVNj6KxhT-g_3J71q23ZxpXUXESnFTaBLn_mOHq_v3ubzSeL54fH2XQxMVkquoniBZaqUiZNpQCDAlMll5wQcxSVkJBQppJlJbMiywmzQpGRiqQkURk0Ih1H10NvjY3eertBf9AOrZ5PF_p4A55AkvNkz3v2amC33n3uKHR6Y4OhpsGW3C5oLiWkacqLrEfVgBrvQvBU_XZz0Eeveq3_vOqjVw1K91776O0Qpf7tvSWvg7HUGiqtJ9Pp0tn_S74BxmqHHw</recordid><startdate>20151201</startdate><enddate>20151201</enddate><creator>Di Cesare, N.</creator><creator>Chamoret, D.</creator><creator>Domaszewski, M.</creator><general>Elsevier Ltd</general><general>Elsevier</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SU</scope><scope>7TB</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>1XC</scope><scope>VOOES</scope></search><sort><creationdate>20151201</creationdate><title>A new hybrid PSO algorithm based on a stochastic Markov chain model</title><author>Di Cesare, N. ; Chamoret, D. ; Domaszewski, M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c435t-819ad8f8c33750ca5a387b1eaa6a5f5702e482bf74946ea498ec78e77e5fcac53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Algorithms</topic><topic>Markov chains</topic><topic>Materials and structures in mechanics</topic><topic>Mathematical models</topic><topic>Mathematics</topic><topic>Mechanical engineering</topic><topic>Mechanics</topic><topic>Mechanics of materials</topic><topic>Optimization</topic><topic>Optimization and Control</topic><topic>PageRank</topic><topic>Particle Swarm Optimization</topic><topic>Physics</topic><topic>Population topology</topic><topic>Search engines</topic><topic>Solid mechanics</topic><topic>Stochasticity</topic><topic>Structural mechanics</topic><topic>Topology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Di Cesare, N.</creatorcontrib><creatorcontrib>Chamoret, D.</creatorcontrib><creatorcontrib>Domaszewski, M.</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Environmental Engineering Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><jtitle>Advances in engineering software (1992)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Di Cesare, N.</au><au>Chamoret, D.</au><au>Domaszewski, M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A new hybrid PSO algorithm based on a stochastic Markov chain model</atitle><jtitle>Advances in engineering software (1992)</jtitle><date>2015-12-01</date><risdate>2015</risdate><volume>90</volume><spage>127</spage><epage>137</epage><pages>127-137</pages><issn>0965-9978</issn><abstract>•Development of a new hybrid PSO algorithm.•Parallelism with a Markov chain model.•Testing of the newly developed algorithm on classic benchmark functions.
Based on the recent research concerning the PageRank Algorithm used in the famous search engine Google [1], a new Inverse-PageRank-Particle Swarm Optimizer (I-PR-PSO) is presented in order to improve the performances of classic PSO. The resulted algorithm uses a stochastic Markov chain model to define an intelligent topological structure of the swarm’s population, in which the better particles have an important influence on the others. In the presented experiments, calculations on some benchmark functions classically used to test optimization methods are performed, and the results are compared to different versions of the standard PSO, that is using different topological structures of the population. The experimental results show that I-PR-PSO can converge quicker on the tested functions, and can find better results in the solution domain than its tested peers.</abstract><pub>Elsevier Ltd</pub><doi>10.1016/j.advengsoft.2015.08.005</doi><tpages>11</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0965-9978 |
| ispartof | Advances in engineering software (1992), 2015-12, Vol.90, p.127-137 |
| issn | 0965-9978 |
| language | eng |
| recordid | cdi_hal_primary_oai_HAL_hal_01202612v1 |
| source | ScienceDirect Journals (5 years ago - present) |
| subjects | Algorithms Markov chains Materials and structures in mechanics Mathematical models Mathematics Mechanical engineering Mechanics Mechanics of materials Optimization Optimization and Control PageRank Particle Swarm Optimization Physics Population topology Search engines Solid mechanics Stochasticity Structural mechanics Topology |
| title | A new hybrid PSO algorithm based on a stochastic Markov chain model |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-04T16%3A43%3A11IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_hal_p&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20new%20hybrid%20PSO%20algorithm%20based%20on%20a%20stochastic%20Markov%20chain%20model&rft.jtitle=Advances%20in%20engineering%20software%20(1992)&rft.au=Di%20Cesare,%20N.&rft.date=2015-12-01&rft.volume=90&rft.spage=127&rft.epage=137&rft.pages=127-137&rft.issn=0965-9978&rft_id=info:doi/10.1016/j.advengsoft.2015.08.005&rft_dat=%3Cproquest_hal_p%3E1770333194%3C/proquest_hal_p%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1770333194&rft_id=info:pmid/&rft_els_id=S0965997815001234&rfr_iscdi=true |