Parallel Ant Colony Optimization for the Traveling Salesman Problem

There are two reasons for parallelizing a metaheuristic if one is interested in performance: (i) given a fixed time to search, the aim is to increase the quality of the solutions found in that time; (ii) given a fixed solution quality, the aim is to reduce the time needed to find a solution not wors...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Manfrin, Max, Birattari, Mauro, Stützle, Thomas, Dorigo, Marco
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Applied sciences Artificial intelligence Computer science control theory systems Computer systems and distributed systems. User interface Exact sciences and technology Message Passing Interface Parallel Algorithm Parallel Model Software Travel Salesman Problem
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

container_end_page	234
container_issue
container_start_page	224
container_title
container_volume
creator	Manfrin, Max Birattari, Mauro Stützle, Thomas Dorigo, Marco
description	There are two reasons for parallelizing a metaheuristic if one is interested in performance: (i) given a fixed time to search, the aim is to increase the quality of the solutions found in that time; (ii) given a fixed solution quality, the aim is to reduce the time needed to find a solution not worse than that quality. In this article, we study the impact of communication when we parallelize a high-performing ant colony optimization (ACO) algorithm for the traveling salesman problem using message passing libraries. In particular, we examine synchronous and asynchronous communications on different interconnection topologies. We find that the simplest way of parallelizing the ACO algorithms, based on parallel independent runs, is surprisingly effective; we give some reasons as to why this is the case.
doi_str_mv	10.1007/11839088_20
format	Conference Proceeding
fullrecord	<record><control><sourceid>pascalfrancis_sprin</sourceid><recordid>TN_cdi_pascalfrancis_primary_20014691</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>20014691</sourcerecordid><originalsourceid>FETCH-LOGICAL-p219t-c351310d789c09f74329aea26fd3fdff3350d821f8a31bbb6d818cf8f787ba223</originalsourceid><addsrcrecordid>eNpNkEtLxDAUheMLHMdZ-QeyceGimpvbR7Icii8YmAHHdbltk7GaPkiKMP56KyPo3Ry453A4fIxdgbgFIbI7AIVaKFVIccQuMIkFqnh6HbMZpAARYqxP_gwpTtlMoJCRzmI8Z4sQ3sV0CKlEPWP5hjw5ZxxfdiPPe9d3e74exqZtvmhs-o7b3vPxzfCtp0_jmm7HX8iZ0FLHN74vnWkv2ZklF8ziV-fs9eF-mz9Fq_Xjc75cRYMEPUYVJoAg6kzpSmg7zZGaDMnU1mhraxETUSsJVhFCWZZprUBVVtlMZSVJiXN2fegdKFTkrKeuakIx-KYlv5-ACIhTDVPu5pALk9XtjC_Kvv8IBYjiB2HxDyF-A-VmXbY</addsrcrecordid><sourcetype>Index Database</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Parallel Ant Colony Optimization for the Traveling Salesman Problem</title><source>Springer Books</source><creator>Manfrin, Max ; Birattari, Mauro ; Stützle, Thomas ; Dorigo, Marco</creator><contributor>Martinoli, Alcherio ; Gambardella, Luca Maria ; Poli, Riccardo ; Dorigo, Marco ; Birattari, Mauro ; Stützle, Thomas</contributor><creatorcontrib>Manfrin, Max ; Birattari, Mauro ; Stützle, Thomas ; Dorigo, Marco ; Martinoli, Alcherio ; Gambardella, Luca Maria ; Poli, Riccardo ; Dorigo, Marco ; Birattari, Mauro ; Stützle, Thomas</creatorcontrib><description>There are two reasons for parallelizing a metaheuristic if one is interested in performance: (i) given a fixed time to search, the aim is to increase the quality of the solutions found in that time; (ii) given a fixed solution quality, the aim is to reduce the time needed to find a solution not worse than that quality. In this article, we study the impact of communication when we parallelize a high-performing ant colony optimization (ACO) algorithm for the traveling salesman problem using message passing libraries. In particular, we examine synchronous and asynchronous communications on different interconnection topologies. We find that the simplest way of parallelizing the ACO algorithms, based on parallel independent runs, is surprisingly effective; we give some reasons as to why this is the case.</description><identifier>ISSN: 0302-9743</identifier><identifier>ISBN: 3540384820</identifier><identifier>ISBN: 9783540384823</identifier><identifier>EISSN: 1611-3349</identifier><identifier>EISBN: 3540384839</identifier><identifier>EISBN: 9783540384830</identifier><identifier>DOI: 10.1007/11839088_20</identifier><language>eng</language><publisher>Berlin, Heidelberg: Springer Berlin Heidelberg</publisher><subject>Applied sciences ; Artificial intelligence ; Computer science; control theory; systems ; Computer systems and distributed systems. User interface ; Exact sciences and technology ; Message Passing Interface ; Parallel Algorithm ; Parallel Model ; Software ; Travel Salesman Problem</subject><ispartof>Ant Colony Optimization and Swarm Intelligence, 2006, p.224-234</ispartof><rights>Springer-Verlag Berlin Heidelberg 2006</rights><rights>2008 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/11839088_20$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/11839088_20$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>310,311,781,782,786,791,792,795,4052,4053,27932,38262,41449,42518</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=20014691$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><contributor>Martinoli, Alcherio</contributor><contributor>Gambardella, Luca Maria</contributor><contributor>Poli, Riccardo</contributor><contributor>Dorigo, Marco</contributor><contributor>Birattari, Mauro</contributor><contributor>Stützle, Thomas</contributor><creatorcontrib>Manfrin, Max</creatorcontrib><creatorcontrib>Birattari, Mauro</creatorcontrib><creatorcontrib>Stützle, Thomas</creatorcontrib><creatorcontrib>Dorigo, Marco</creatorcontrib><title>Parallel Ant Colony Optimization for the Traveling Salesman Problem</title><title>Ant Colony Optimization and Swarm Intelligence</title><description>There are two reasons for parallelizing a metaheuristic if one is interested in performance: (i) given a fixed time to search, the aim is to increase the quality of the solutions found in that time; (ii) given a fixed solution quality, the aim is to reduce the time needed to find a solution not worse than that quality. In this article, we study the impact of communication when we parallelize a high-performing ant colony optimization (ACO) algorithm for the traveling salesman problem using message passing libraries. In particular, we examine synchronous and asynchronous communications on different interconnection topologies. We find that the simplest way of parallelizing the ACO algorithms, based on parallel independent runs, is surprisingly effective; we give some reasons as to why this is the case.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Computer systems and distributed systems. User interface</subject><subject>Exact sciences and technology</subject><subject>Message Passing Interface</subject><subject>Parallel Algorithm</subject><subject>Parallel Model</subject><subject>Software</subject><subject>Travel Salesman Problem</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>3540384820</isbn><isbn>9783540384823</isbn><isbn>3540384839</isbn><isbn>9783540384830</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2006</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNpNkEtLxDAUheMLHMdZ-QeyceGimpvbR7Icii8YmAHHdbltk7GaPkiKMP56KyPo3Ry453A4fIxdgbgFIbI7AIVaKFVIccQuMIkFqnh6HbMZpAARYqxP_gwpTtlMoJCRzmI8Z4sQ3sV0CKlEPWP5hjw5ZxxfdiPPe9d3e74exqZtvmhs-o7b3vPxzfCtp0_jmm7HX8iZ0FLHN74vnWkv2ZklF8ziV-fs9eF-mz9Fq_Xjc75cRYMEPUYVJoAg6kzpSmg7zZGaDMnU1mhraxETUSsJVhFCWZZprUBVVtlMZSVJiXN2fegdKFTkrKeuakIx-KYlv5-ACIhTDVPu5pALk9XtjC_Kvv8IBYjiB2HxDyF-A-VmXbY</recordid><startdate>2006</startdate><enddate>2006</enddate><creator>Manfrin, Max</creator><creator>Birattari, Mauro</creator><creator>Stützle, Thomas</creator><creator>Dorigo, Marco</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>2006</creationdate><title>Parallel Ant Colony Optimization for the Traveling Salesman Problem</title><author>Manfrin, Max ; Birattari, Mauro ; Stützle, Thomas ; Dorigo, Marco</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p219t-c351310d789c09f74329aea26fd3fdff3350d821f8a31bbb6d818cf8f787ba223</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computer science; control theory; systems</topic><topic>Computer systems and distributed systems. User interface</topic><topic>Exact sciences and technology</topic><topic>Message Passing Interface</topic><topic>Parallel Algorithm</topic><topic>Parallel Model</topic><topic>Software</topic><topic>Travel Salesman Problem</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Manfrin, Max</creatorcontrib><creatorcontrib>Birattari, Mauro</creatorcontrib><creatorcontrib>Stützle, Thomas</creatorcontrib><creatorcontrib>Dorigo, Marco</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Manfrin, Max</au><au>Birattari, Mauro</au><au>Stützle, Thomas</au><au>Dorigo, Marco</au><au>Martinoli, Alcherio</au><au>Gambardella, Luca Maria</au><au>Poli, Riccardo</au><au>Dorigo, Marco</au><au>Birattari, Mauro</au><au>Stützle, Thomas</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Parallel Ant Colony Optimization for the Traveling Salesman Problem</atitle><btitle>Ant Colony Optimization and Swarm Intelligence</btitle><date>2006</date><risdate>2006</risdate><spage>224</spage><epage>234</epage><pages>224-234</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>3540384820</isbn><isbn>9783540384823</isbn><eisbn>3540384839</eisbn><eisbn>9783540384830</eisbn><abstract>There are two reasons for parallelizing a metaheuristic if one is interested in performance: (i) given a fixed time to search, the aim is to increase the quality of the solutions found in that time; (ii) given a fixed solution quality, the aim is to reduce the time needed to find a solution not worse than that quality. In this article, we study the impact of communication when we parallelize a high-performing ant colony optimization (ACO) algorithm for the traveling salesman problem using message passing libraries. In particular, we examine synchronous and asynchronous communications on different interconnection topologies. We find that the simplest way of parallelizing the ACO algorithms, based on parallel independent runs, is surprisingly effective; we give some reasons as to why this is the case.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/11839088_20</doi><tpages>11</tpages></addata></record>
fulltext	fulltext
identifier	ISSN: 0302-9743
ispartof	Ant Colony Optimization and Swarm Intelligence, 2006, p.224-234
issn	0302-9743 1611-3349
language	eng
recordid	cdi_pascalfrancis_primary_20014691
source	Springer Books
subjects	Applied sciences Artificial intelligence Computer science control theory systems Computer systems and distributed systems. User interface Exact sciences and technology Message Passing Interface Parallel Algorithm Parallel Model Software Travel Salesman Problem
title	Parallel Ant Colony Optimization for the Traveling Salesman Problem
url	https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-04T10%3A19%3A12IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-pascalfrancis_sprin&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Parallel%20Ant%20Colony%20Optimization%20for%20the%20Traveling%20Salesman%20Problem&rft.btitle=Ant%20Colony%20Optimization%20and%20Swarm%20Intelligence&rft.au=Manfrin,%20Max&rft.date=2006&rft.spage=224&rft.epage=234&rft.pages=224-234&rft.issn=0302-9743&rft.eissn=1611-3349&rft.isbn=3540384820&rft.isbn_list=9783540384823&rft_id=info:doi/10.1007/11839088_20&rft_dat=%3Cpascalfrancis_sprin%3E20014691%3C/pascalfrancis_sprin%3E%3Curl%3E%3C/url%3E&rft.eisbn=3540384839&rft.eisbn_list=9783540384830&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true