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Parallel strategies for a multi-criteria GRASP algorithm
This paper proposes different strategies of parallelizing a multi-criteria GRASP (greedy randomized adaptive search problem) algorithm. The parallel GRASP algorithm is applied to the multi-criteria minimum spanning tree problem, which is NP-hard. In this problem, a vector of costs is defined for eac...
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creator | Vianna, D.S. Arroyo, J.E.C. Vieira, P.S. de Azeredo, T.R. |
description | This paper proposes different strategies of parallelizing a multi-criteria GRASP (greedy randomized adaptive search problem) algorithm. The parallel GRASP algorithm is applied to the multi-criteria minimum spanning tree problem, which is NP-hard. In this problem, a vector of costs is defined for each edge of the graph and the goal is to find all the efficient or Pareto optimal spanning trees (solutions). Each process finds a subset of efficient solutions. These subsets are joined using different strategies to obtain the final set of efficient solutions. The multi-criteria GRASP algorithm with the different parallel strategies are tested on complete graphs with n = 20, 30 and 50 nodes and r = 2 and 3 criteria. The computational results show that the proposed parallel algorithms reduce the execution time and the results obtained by the sequential version were improved. |
doi_str_mv | 10.1109/SCCC.2005.1587873 |
format | conference_proceeding |
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The parallel GRASP algorithm is applied to the multi-criteria minimum spanning tree problem, which is NP-hard. In this problem, a vector of costs is defined for each edge of the graph and the goal is to find all the efficient or Pareto optimal spanning trees (solutions). Each process finds a subset of efficient solutions. These subsets are joined using different strategies to obtain the final set of efficient solutions. The multi-criteria GRASP algorithm with the different parallel strategies are tested on complete graphs with n = 20, 30 and 50 nodes and r = 2 and 3 criteria. 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The computational results show that the proposed parallel algorithms reduce the execution time and the results obtained by the sequential version were improved.</description><subject>Cities and towns</subject><subject>Concurrent computing</subject><subject>Cost function</subject><subject>Design optimization</subject><subject>Large-scale systems</subject><subject>minimum spanning tree</subject><subject>multi-criteria combinatorial optimization</subject><subject>Parallel algorithms</subject><subject>Parallel GRASP algorithm</subject><subject>Search problems</subject><subject>Telecommunication traffic</subject><subject>Testing</subject><subject>Tree graphs</subject><issn>1522-4902</issn><issn>2691-0632</issn><isbn>0769524915</isbn><isbn>9780769524917</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2005</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotj01Lw0AQQBc_wLb6A8TL_oHEndnd7OyxBK1CwWL1XKbJpK4kVDbx4L9XsKcH7_DgKXULpgQw8X5b13WJxvgSPAUK9kzNsIpQmMriuZqbUEWPLoK_UDPwiIWLBq_UfBw_jUGDEWaKNpy576XX45R5kkOSUXfHrFkP3_2UiianSXJivXpdbjea-8Pxz3wM1-qy436UmxMX6v3x4a1-KtYvq-d6uS4SAk1F1bUG9hSijR2KgGsZKEYSxy3tLfmGHRD5NrANHaGjBm0IAmINeo52oe7-u0lEdl85DZx_dqdj-wsyMEbi</recordid><startdate>2005</startdate><enddate>2005</enddate><creator>Vianna, D.S.</creator><creator>Arroyo, J.E.C.</creator><creator>Vieira, P.S.</creator><creator>de Azeredo, T.R.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>2005</creationdate><title>Parallel strategies for a multi-criteria GRASP algorithm</title><author>Vianna, D.S. ; Arroyo, J.E.C. ; Vieira, P.S. ; de Azeredo, T.R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i218t-6fd01b87939f2ee14da18998e4ad8b385ca41885d7a37f8248c2377e1e3025a93</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Cities and towns</topic><topic>Concurrent computing</topic><topic>Cost function</topic><topic>Design optimization</topic><topic>Large-scale systems</topic><topic>minimum spanning tree</topic><topic>multi-criteria combinatorial optimization</topic><topic>Parallel algorithms</topic><topic>Parallel GRASP algorithm</topic><topic>Search problems</topic><topic>Telecommunication traffic</topic><topic>Testing</topic><topic>Tree graphs</topic><toplevel>online_resources</toplevel><creatorcontrib>Vianna, D.S.</creatorcontrib><creatorcontrib>Arroyo, J.E.C.</creatorcontrib><creatorcontrib>Vieira, P.S.</creatorcontrib><creatorcontrib>de Azeredo, T.R.</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 Xplore</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>Vianna, D.S.</au><au>Arroyo, J.E.C.</au><au>Vieira, P.S.</au><au>de Azeredo, T.R.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Parallel strategies for a multi-criteria GRASP algorithm</atitle><btitle>XXV International Conference of the Chilean Computer Science Society (SCCC'05)</btitle><stitle>SCCC</stitle><date>2005</date><risdate>2005</risdate><spage>7 pp.</spage><pages>7 pp.-</pages><issn>1522-4902</issn><eissn>2691-0632</eissn><isbn>0769524915</isbn><isbn>9780769524917</isbn><abstract>This paper proposes different strategies of parallelizing a multi-criteria GRASP (greedy randomized adaptive search problem) algorithm. The parallel GRASP algorithm is applied to the multi-criteria minimum spanning tree problem, which is NP-hard. In this problem, a vector of costs is defined for each edge of the graph and the goal is to find all the efficient or Pareto optimal spanning trees (solutions). Each process finds a subset of efficient solutions. These subsets are joined using different strategies to obtain the final set of efficient solutions. The multi-criteria GRASP algorithm with the different parallel strategies are tested on complete graphs with n = 20, 30 and 50 nodes and r = 2 and 3 criteria. The computational results show that the proposed parallel algorithms reduce the execution time and the results obtained by the sequential version were improved.</abstract><pub>IEEE</pub><doi>10.1109/SCCC.2005.1587873</doi><oa>free_for_read</oa></addata></record> |
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source | IEEE Electronic Library (IEL) Conference Proceedings |
subjects | Cities and towns Concurrent computing Cost function Design optimization Large-scale systems minimum spanning tree multi-criteria combinatorial optimization Parallel algorithms Parallel GRASP algorithm Search problems Telecommunication traffic Testing Tree graphs |
title | Parallel strategies for a multi-criteria GRASP algorithm |
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