Parallel ACO with a Ring Neighborhood for Dynamic TSP
The current paper introduces a new parallel computing technique based on ant colony optimization for a dynamic routing problem. Ant Colony Optimization is a metaheurisitc that is able to solve large scale optimization problems. In the dynamic traveling salesman problem, the distances between cities...
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| Autor*in: |
Pintea, Camelia M. [verfasserIn] Crisan, Gloria Cerasela [author] Manea, Mihai [author] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2012 |
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| Schlagwörter: |
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| Umfang: |
Online-Ressource |
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IGI Global InfoSci Journals Archive 2000 - 2012 |
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| Übergeordnetes Werk: |
In: Journal of information technology research - Hershey, PA : IGI Global, 2008, 5(2012), 4, Seite 1-13 |
| Übergeordnetes Werk: |
volume:5 ; year:2012 ; number:4 ; pages:1-13 |
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| DOI / URN: |
10.4018/jitr.2012100101 |
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NLEJ244520100 |
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| 520 | |a The current paper introduces a new parallel computing technique based on ant colony optimization for a dynamic routing problem. Ant Colony Optimization is a metaheurisitc that is able to solve large scale optimization problems. In the dynamic traveling salesman problem, the distances between cities as travel times are no longer fixed. The new technique uses a parallel model for a problem variant that allows a slight movement of nodes within their neighborhoods. The algorithm is tested with success on several large data sets. The paper concludes with a discussion of the results provided by both the sequential and parallel approaches and calls for further research on the subject | ||
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10.4018/jitr.2012100101 doi (DE-627)NLEJ244520100 (VZGNL)10.4018/jitr.2012100101 DE-627 ger DE-627 rakwb eng Pintea, Camelia M. verfasserin aut Parallel ACO with a Ring Neighborhood for Dynamic TSP 2012 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The current paper introduces a new parallel computing technique based on ant colony optimization for a dynamic routing problem. Ant Colony Optimization is a metaheurisitc that is able to solve large scale optimization problems. In the dynamic traveling salesman problem, the distances between cities as travel times are no longer fixed. The new technique uses a parallel model for a problem variant that allows a slight movement of nodes within their neighborhoods. The algorithm is tested with success on several large data sets. The paper concludes with a discussion of the results provided by both the sequential and parallel approaches and calls for further research on the subject IGI Global InfoSci Journals Archive 2000 - 2012 Algorithm Ant Colony Optimization Artificial Ants Dynamic Problems Dynamic Traveling Salesman Problem (DTSP) Traveling Salesman Problem Crisan, Gloria Cerasela author aut Manea, Mihai author aut In Journal of information technology research Hershey, PA : IGI Global, 2008 5(2012), 4, Seite 1-13 Online-Ressource (DE-627)NLEJ244419728 (DE-600)2403406-X 1938-7865 nnns volume:5 year:2012 number:4 pages:1-13 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jitr.2012100101 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jitr.2012100101&buylink=true text/html Abstract Deutschlandweit zugänglich ZDB-1-GIS GBV_NL_ARTICLE AR 5 2012 4 1-13 |
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10.4018/jitr.2012100101 doi (DE-627)NLEJ244520100 (VZGNL)10.4018/jitr.2012100101 DE-627 ger DE-627 rakwb eng Pintea, Camelia M. verfasserin aut Parallel ACO with a Ring Neighborhood for Dynamic TSP 2012 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The current paper introduces a new parallel computing technique based on ant colony optimization for a dynamic routing problem. Ant Colony Optimization is a metaheurisitc that is able to solve large scale optimization problems. In the dynamic traveling salesman problem, the distances between cities as travel times are no longer fixed. The new technique uses a parallel model for a problem variant that allows a slight movement of nodes within their neighborhoods. The algorithm is tested with success on several large data sets. The paper concludes with a discussion of the results provided by both the sequential and parallel approaches and calls for further research on the subject IGI Global InfoSci Journals Archive 2000 - 2012 Algorithm Ant Colony Optimization Artificial Ants Dynamic Problems Dynamic Traveling Salesman Problem (DTSP) Traveling Salesman Problem Crisan, Gloria Cerasela author aut Manea, Mihai author aut In Journal of information technology research Hershey, PA : IGI Global, 2008 5(2012), 4, Seite 1-13 Online-Ressource (DE-627)NLEJ244419728 (DE-600)2403406-X 1938-7865 nnns volume:5 year:2012 number:4 pages:1-13 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jitr.2012100101 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jitr.2012100101&buylink=true text/html Abstract Deutschlandweit zugänglich ZDB-1-GIS GBV_NL_ARTICLE AR 5 2012 4 1-13 |
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10.4018/jitr.2012100101 doi (DE-627)NLEJ244520100 (VZGNL)10.4018/jitr.2012100101 DE-627 ger DE-627 rakwb eng Pintea, Camelia M. verfasserin aut Parallel ACO with a Ring Neighborhood for Dynamic TSP 2012 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The current paper introduces a new parallel computing technique based on ant colony optimization for a dynamic routing problem. Ant Colony Optimization is a metaheurisitc that is able to solve large scale optimization problems. In the dynamic traveling salesman problem, the distances between cities as travel times are no longer fixed. The new technique uses a parallel model for a problem variant that allows a slight movement of nodes within their neighborhoods. The algorithm is tested with success on several large data sets. The paper concludes with a discussion of the results provided by both the sequential and parallel approaches and calls for further research on the subject IGI Global InfoSci Journals Archive 2000 - 2012 Algorithm Ant Colony Optimization Artificial Ants Dynamic Problems Dynamic Traveling Salesman Problem (DTSP) Traveling Salesman Problem Crisan, Gloria Cerasela author aut Manea, Mihai author aut In Journal of information technology research Hershey, PA : IGI Global, 2008 5(2012), 4, Seite 1-13 Online-Ressource (DE-627)NLEJ244419728 (DE-600)2403406-X 1938-7865 nnns volume:5 year:2012 number:4 pages:1-13 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jitr.2012100101 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jitr.2012100101&buylink=true text/html Abstract Deutschlandweit zugänglich ZDB-1-GIS GBV_NL_ARTICLE AR 5 2012 4 1-13 |
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10.4018/jitr.2012100101 doi (DE-627)NLEJ244520100 (VZGNL)10.4018/jitr.2012100101 DE-627 ger DE-627 rakwb eng Pintea, Camelia M. verfasserin aut Parallel ACO with a Ring Neighborhood for Dynamic TSP 2012 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The current paper introduces a new parallel computing technique based on ant colony optimization for a dynamic routing problem. Ant Colony Optimization is a metaheurisitc that is able to solve large scale optimization problems. In the dynamic traveling salesman problem, the distances between cities as travel times are no longer fixed. The new technique uses a parallel model for a problem variant that allows a slight movement of nodes within their neighborhoods. The algorithm is tested with success on several large data sets. The paper concludes with a discussion of the results provided by both the sequential and parallel approaches and calls for further research on the subject IGI Global InfoSci Journals Archive 2000 - 2012 Algorithm Ant Colony Optimization Artificial Ants Dynamic Problems Dynamic Traveling Salesman Problem (DTSP) Traveling Salesman Problem Crisan, Gloria Cerasela author aut Manea, Mihai author aut In Journal of information technology research Hershey, PA : IGI Global, 2008 5(2012), 4, Seite 1-13 Online-Ressource (DE-627)NLEJ244419728 (DE-600)2403406-X 1938-7865 nnns volume:5 year:2012 number:4 pages:1-13 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jitr.2012100101 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jitr.2012100101&buylink=true text/html Abstract Deutschlandweit zugänglich ZDB-1-GIS GBV_NL_ARTICLE AR 5 2012 4 1-13 |
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The current paper introduces a new parallel computing technique based on ant colony optimization for a dynamic routing problem. Ant Colony Optimization is a metaheurisitc that is able to solve large scale optimization problems. In the dynamic traveling salesman problem, the distances between cities as travel times are no longer fixed. The new technique uses a parallel model for a problem variant that allows a slight movement of nodes within their neighborhoods. The algorithm is tested with success on several large data sets. The paper concludes with a discussion of the results provided by both the sequential and parallel approaches and calls for further research on the subject |
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The current paper introduces a new parallel computing technique based on ant colony optimization for a dynamic routing problem. Ant Colony Optimization is a metaheurisitc that is able to solve large scale optimization problems. In the dynamic traveling salesman problem, the distances between cities as travel times are no longer fixed. The new technique uses a parallel model for a problem variant that allows a slight movement of nodes within their neighborhoods. The algorithm is tested with success on several large data sets. The paper concludes with a discussion of the results provided by both the sequential and parallel approaches and calls for further research on the subject |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">NLEJ244520100</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240202180316.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">150605s2012 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.4018/jitr.2012100101</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)NLEJ244520100</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(VZGNL)10.4018/jitr.2012100101</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Pintea, Camelia M.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Parallel ACO with a Ring Neighborhood for Dynamic TSP</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2012</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The current paper introduces a new parallel computing technique based on ant colony optimization for a dynamic routing problem. Ant Colony Optimization is a metaheurisitc that is able to solve large scale optimization problems. In the dynamic traveling salesman problem, the distances between cities as travel times are no longer fixed. The new technique uses a parallel model for a problem variant that allows a slight movement of nodes within their neighborhoods. The algorithm is tested with success on several large data sets. 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