Loading…

Approximative graph pyramid solution of the E-TSP

The traveling salesman problem (TSP) is difficult to solve for input instances with large number of cities. Instead of finding the solution for an input with a large number of cities, the problem is transformed into a simpler form containing smaller number of cities, which is then solved optimally....

Full description

Saved in:
Bibliographic Details
Published in:Image and vision computing 2009-06, Vol.27 (7), p.887-896
Main Authors: Haxhimusa, Yll, Kropatsch, Walter G., Pizlo, Zygmunt, Ion, Adrian
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c337t-274198e637632f85b86c17ba185b311d7a6ee0ad612295ef87418c538870b2cf3
cites cdi_FETCH-LOGICAL-c337t-274198e637632f85b86c17ba185b311d7a6ee0ad612295ef87418c538870b2cf3
container_end_page 896
container_issue 7
container_start_page 887
container_title Image and vision computing
container_volume 27
creator Haxhimusa, Yll
Kropatsch, Walter G.
Pizlo, Zygmunt
Ion, Adrian
description The traveling salesman problem (TSP) is difficult to solve for input instances with large number of cities. Instead of finding the solution for an input with a large number of cities, the problem is transformed into a simpler form containing smaller number of cities, which is then solved optimally. Graph pyramid solution strategies, using Borůvka’s minimum spanning tree step, convert, in a bottom-up processing, a 2D Euclidean TSP problem with a large number of cities into successively smaller problems (graphs) with similar layout and solution, until the number of cities is small enough to seek the optimal solution. Expanding this tour solution in a top-down manner, to the lower levels of the pyramid, leads to an approximate solution. The new model has an adaptive spatial structure and it simulates visual acuity and visual attention. The model solves the TSP problem sequentially, by moving attention from city to city, and the quality of the solutions is similar to the solutions produced by humans. The graph pyramid data structures and processing strategies provide good methods for finding near-optimal solutions for computationally hard problems. Isolating processing used by humans to solve computationally hard problems is of general importance to psychology community and might lead to advances in pattern recognition.
doi_str_mv 10.1016/j.imavis.2008.06.016
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_34521013</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0262885608001388</els_id><sourcerecordid>34521013</sourcerecordid><originalsourceid>FETCH-LOGICAL-c337t-274198e637632f85b86c17ba185b311d7a6ee0ad612295ef87418c538870b2cf3</originalsourceid><addsrcrecordid>eNp9UMFKAzEUDKJgrf6Bhz152_Ul6SbpRSilVqGgYD2HbPatTdk2a7It9u9NWc-e3jDMDG-GkHsKBQUqHreF25mjiwUDUAWIIpEXZESVZLmiXF2SETCRsCrFNbmJcQsAEuR0ROis64L_Sf7eHTH7CqbbZN0pmJ2rs-jbQ-_8PvNN1m8wW-Trj_dbctWYNuLd3x2Tz-fFev6Sr96Wr_PZKrecyz5nckKnCgWXgrNGlZUSlsrK0AQ5pbU0AhFMLShj0xIblfTKllwpCRWzDR-ThyE3_fd9wNjrnYsW29bs0R-i5pOSpfY8CSeD0AYfY8BGdyH1CSdNQZ_30Vs97KPP-2gQOpHJ9jTYMJU4Ogw6Wod7i7ULaHtde_d_wC9jMW6i</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>34521013</pqid></control><display><type>article</type><title>Approximative graph pyramid solution of the E-TSP</title><source>ScienceDirect Freedom Collection</source><creator>Haxhimusa, Yll ; Kropatsch, Walter G. ; Pizlo, Zygmunt ; Ion, Adrian</creator><creatorcontrib>Haxhimusa, Yll ; Kropatsch, Walter G. ; Pizlo, Zygmunt ; Ion, Adrian</creatorcontrib><description>The traveling salesman problem (TSP) is difficult to solve for input instances with large number of cities. Instead of finding the solution for an input with a large number of cities, the problem is transformed into a simpler form containing smaller number of cities, which is then solved optimally. Graph pyramid solution strategies, using Borůvka’s minimum spanning tree step, convert, in a bottom-up processing, a 2D Euclidean TSP problem with a large number of cities into successively smaller problems (graphs) with similar layout and solution, until the number of cities is small enough to seek the optimal solution. Expanding this tour solution in a top-down manner, to the lower levels of the pyramid, leads to an approximate solution. The new model has an adaptive spatial structure and it simulates visual acuity and visual attention. The model solves the TSP problem sequentially, by moving attention from city to city, and the quality of the solutions is similar to the solutions produced by humans. The graph pyramid data structures and processing strategies provide good methods for finding near-optimal solutions for computationally hard problems. Isolating processing used by humans to solve computationally hard problems is of general importance to psychology community and might lead to advances in pattern recognition.</description><identifier>ISSN: 0262-8856</identifier><identifier>EISSN: 1872-8138</identifier><identifier>DOI: 10.1016/j.imavis.2008.06.016</identifier><language>eng</language><publisher>Elsevier B.V</publisher><subject>Borůvka’s minimum spanning tree ; Graph pyramids ; Hierarchical representation ; Human problem solving ; Traveling salesman problem</subject><ispartof>Image and vision computing, 2009-06, Vol.27 (7), p.887-896</ispartof><rights>2008 Elsevier B.V.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c337t-274198e637632f85b86c17ba185b311d7a6ee0ad612295ef87418c538870b2cf3</citedby><cites>FETCH-LOGICAL-c337t-274198e637632f85b86c17ba185b311d7a6ee0ad612295ef87418c538870b2cf3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Haxhimusa, Yll</creatorcontrib><creatorcontrib>Kropatsch, Walter G.</creatorcontrib><creatorcontrib>Pizlo, Zygmunt</creatorcontrib><creatorcontrib>Ion, Adrian</creatorcontrib><title>Approximative graph pyramid solution of the E-TSP</title><title>Image and vision computing</title><description>The traveling salesman problem (TSP) is difficult to solve for input instances with large number of cities. Instead of finding the solution for an input with a large number of cities, the problem is transformed into a simpler form containing smaller number of cities, which is then solved optimally. Graph pyramid solution strategies, using Borůvka’s minimum spanning tree step, convert, in a bottom-up processing, a 2D Euclidean TSP problem with a large number of cities into successively smaller problems (graphs) with similar layout and solution, until the number of cities is small enough to seek the optimal solution. Expanding this tour solution in a top-down manner, to the lower levels of the pyramid, leads to an approximate solution. The new model has an adaptive spatial structure and it simulates visual acuity and visual attention. The model solves the TSP problem sequentially, by moving attention from city to city, and the quality of the solutions is similar to the solutions produced by humans. The graph pyramid data structures and processing strategies provide good methods for finding near-optimal solutions for computationally hard problems. Isolating processing used by humans to solve computationally hard problems is of general importance to psychology community and might lead to advances in pattern recognition.</description><subject>Borůvka’s minimum spanning tree</subject><subject>Graph pyramids</subject><subject>Hierarchical representation</subject><subject>Human problem solving</subject><subject>Traveling salesman problem</subject><issn>0262-8856</issn><issn>1872-8138</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><recordid>eNp9UMFKAzEUDKJgrf6Bhz152_Ul6SbpRSilVqGgYD2HbPatTdk2a7It9u9NWc-e3jDMDG-GkHsKBQUqHreF25mjiwUDUAWIIpEXZESVZLmiXF2SETCRsCrFNbmJcQsAEuR0ROis64L_Sf7eHTH7CqbbZN0pmJ2rs-jbQ-_8PvNN1m8wW-Trj_dbctWYNuLd3x2Tz-fFev6Sr96Wr_PZKrecyz5nckKnCgWXgrNGlZUSlsrK0AQ5pbU0AhFMLShj0xIblfTKllwpCRWzDR-ThyE3_fd9wNjrnYsW29bs0R-i5pOSpfY8CSeD0AYfY8BGdyH1CSdNQZ_30Vs97KPP-2gQOpHJ9jTYMJU4Ogw6Wod7i7ULaHtde_d_wC9jMW6i</recordid><startdate>20090604</startdate><enddate>20090604</enddate><creator>Haxhimusa, Yll</creator><creator>Kropatsch, Walter G.</creator><creator>Pizlo, Zygmunt</creator><creator>Ion, Adrian</creator><general>Elsevier B.V</general><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>20090604</creationdate><title>Approximative graph pyramid solution of the E-TSP</title><author>Haxhimusa, Yll ; Kropatsch, Walter G. ; Pizlo, Zygmunt ; Ion, Adrian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c337t-274198e637632f85b86c17ba185b311d7a6ee0ad612295ef87418c538870b2cf3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Borůvka’s minimum spanning tree</topic><topic>Graph pyramids</topic><topic>Hierarchical representation</topic><topic>Human problem solving</topic><topic>Traveling salesman problem</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Haxhimusa, Yll</creatorcontrib><creatorcontrib>Kropatsch, Walter G.</creatorcontrib><creatorcontrib>Pizlo, Zygmunt</creatorcontrib><creatorcontrib>Ion, Adrian</creatorcontrib><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>Image and vision computing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Haxhimusa, Yll</au><au>Kropatsch, Walter G.</au><au>Pizlo, Zygmunt</au><au>Ion, Adrian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Approximative graph pyramid solution of the E-TSP</atitle><jtitle>Image and vision computing</jtitle><date>2009-06-04</date><risdate>2009</risdate><volume>27</volume><issue>7</issue><spage>887</spage><epage>896</epage><pages>887-896</pages><issn>0262-8856</issn><eissn>1872-8138</eissn><abstract>The traveling salesman problem (TSP) is difficult to solve for input instances with large number of cities. Instead of finding the solution for an input with a large number of cities, the problem is transformed into a simpler form containing smaller number of cities, which is then solved optimally. Graph pyramid solution strategies, using Borůvka’s minimum spanning tree step, convert, in a bottom-up processing, a 2D Euclidean TSP problem with a large number of cities into successively smaller problems (graphs) with similar layout and solution, until the number of cities is small enough to seek the optimal solution. Expanding this tour solution in a top-down manner, to the lower levels of the pyramid, leads to an approximate solution. The new model has an adaptive spatial structure and it simulates visual acuity and visual attention. The model solves the TSP problem sequentially, by moving attention from city to city, and the quality of the solutions is similar to the solutions produced by humans. The graph pyramid data structures and processing strategies provide good methods for finding near-optimal solutions for computationally hard problems. Isolating processing used by humans to solve computationally hard problems is of general importance to psychology community and might lead to advances in pattern recognition.</abstract><pub>Elsevier B.V</pub><doi>10.1016/j.imavis.2008.06.016</doi><tpages>10</tpages></addata></record>
fulltext fulltext
identifier ISSN: 0262-8856
ispartof Image and vision computing, 2009-06, Vol.27 (7), p.887-896
issn 0262-8856
1872-8138
language eng
recordid cdi_proquest_miscellaneous_34521013
source ScienceDirect Freedom Collection
subjects Borůvka’s minimum spanning tree
Graph pyramids
Hierarchical representation
Human problem solving
Traveling salesman problem
title Approximative graph pyramid solution of the E-TSP
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-02T04%3A03%3A35IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Approximative%20graph%20pyramid%20solution%20of%20the%20E-TSP&rft.jtitle=Image%20and%20vision%20computing&rft.au=Haxhimusa,%20Yll&rft.date=2009-06-04&rft.volume=27&rft.issue=7&rft.spage=887&rft.epage=896&rft.pages=887-896&rft.issn=0262-8856&rft.eissn=1872-8138&rft_id=info:doi/10.1016/j.imavis.2008.06.016&rft_dat=%3Cproquest_cross%3E34521013%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c337t-274198e637632f85b86c17ba185b311d7a6ee0ad612295ef87418c538870b2cf3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=34521013&rft_id=info:pmid/&rfr_iscdi=true