A new and efficient ant-based heuristic method for solving the traveling salesman problem
Abstract: In this paper, we present an efficient metaheuristic approach for solving the problem of the traveling salesman. We introduce the multiple ant clans concept from parallel genetic algorithms to search solution space using different islands to avoid local minima in order to obtain a global m...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Tsai, Cheng-Fa [verfasserIn] Tsai, Chun-Wei [verfasserIn] Tseng, Ching-Chang [verfasserIn] |
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| Format: |
E-Artikel |
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| Erschienen: |
Oxford, UK: Blackwell Publishing ; 2003 |
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| Schlagwörter: |
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| Umfang: |
Online-Ressource |
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| Reproduktion: |
2003 ; Blackwell Publishing Journal Backfiles 1879-2005 |
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| Übergeordnetes Werk: |
In: Expert systems - Oxford [u.a.] : Wiley-Blackwell, 1997, 20(2003), 4, Seite 0 |
| Übergeordnetes Werk: |
volume:20 ; year:2003 ; number:4 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1111/1468-0394.00242 |
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NLEJ24237512X |
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| 520 | |a Abstract: In this paper, we present an efficient metaheuristic approach for solving the problem of the traveling salesman. We introduce the multiple ant clans concept from parallel genetic algorithms to search solution space using different islands to avoid local minima in order to obtain a global minimum for solving the traveling salesman problem. Our simulation results indicate that the proposed novel traveling salesman problem method (called the ACOMAC algorithm) performs better than a promising approach named the ant colony system. This investigation is concerned with a real life logistics system design which optimizes the performance of a logistics system subject to a required service level in the vehicle routing problem. In this work, we also concentrate on developing a vehicle routing model by improving the ant colony system and using the multiple ant clans concept. The simulation results reveal that the proposed method is very effective and potentially useful in solving vehicle routing problems. | ||
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10.1111/1468-0394.00242 doi (DE-627)NLEJ24237512X DE-627 ger DE-627 rakwb Tsai, Cheng-Fa verfasserin aut A new and efficient ant-based heuristic method for solving the traveling salesman problem Oxford, UK Blackwell Publishing 2003 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Abstract: In this paper, we present an efficient metaheuristic approach for solving the problem of the traveling salesman. We introduce the multiple ant clans concept from parallel genetic algorithms to search solution space using different islands to avoid local minima in order to obtain a global minimum for solving the traveling salesman problem. Our simulation results indicate that the proposed novel traveling salesman problem method (called the ACOMAC algorithm) performs better than a promising approach named the ant colony system. This investigation is concerned with a real life logistics system design which optimizes the performance of a logistics system subject to a required service level in the vehicle routing problem. In this work, we also concentrate on developing a vehicle routing model by improving the ant colony system and using the multiple ant clans concept. The simulation results reveal that the proposed method is very effective and potentially useful in solving vehicle routing problems. 2003 Blackwell Publishing Journal Backfiles 1879-2005 |2003|||||||||| traveling salesman problem Tsai, Chun-Wei verfasserin aut Tseng, Ching-Chang verfasserin aut In Expert systems Oxford [u.a.] : Wiley-Blackwell, 1997 20(2003), 4, Seite 0 Online-Ressource (DE-627)NLEJ243925662 (DE-600)2016958-9 1468-0394 nnns volume:20 year:2003 number:4 pages:0 http://dx.doi.org/10.1111/1468-0394.00242 text/html Verlag Deutschlandweit zugänglich Volltext GBV_USEFLAG_U ZDB-1-DJB GBV_NL_ARTICLE AR 20 2003 4 0 |
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10.1111/1468-0394.00242 doi (DE-627)NLEJ24237512X DE-627 ger DE-627 rakwb Tsai, Cheng-Fa verfasserin aut A new and efficient ant-based heuristic method for solving the traveling salesman problem Oxford, UK Blackwell Publishing 2003 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Abstract: In this paper, we present an efficient metaheuristic approach for solving the problem of the traveling salesman. We introduce the multiple ant clans concept from parallel genetic algorithms to search solution space using different islands to avoid local minima in order to obtain a global minimum for solving the traveling salesman problem. Our simulation results indicate that the proposed novel traveling salesman problem method (called the ACOMAC algorithm) performs better than a promising approach named the ant colony system. This investigation is concerned with a real life logistics system design which optimizes the performance of a logistics system subject to a required service level in the vehicle routing problem. In this work, we also concentrate on developing a vehicle routing model by improving the ant colony system and using the multiple ant clans concept. The simulation results reveal that the proposed method is very effective and potentially useful in solving vehicle routing problems. 2003 Blackwell Publishing Journal Backfiles 1879-2005 |2003|||||||||| traveling salesman problem Tsai, Chun-Wei verfasserin aut Tseng, Ching-Chang verfasserin aut In Expert systems Oxford [u.a.] : Wiley-Blackwell, 1997 20(2003), 4, Seite 0 Online-Ressource (DE-627)NLEJ243925662 (DE-600)2016958-9 1468-0394 nnns volume:20 year:2003 number:4 pages:0 http://dx.doi.org/10.1111/1468-0394.00242 text/html Verlag Deutschlandweit zugänglich Volltext GBV_USEFLAG_U ZDB-1-DJB GBV_NL_ARTICLE AR 20 2003 4 0 |
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10.1111/1468-0394.00242 doi (DE-627)NLEJ24237512X DE-627 ger DE-627 rakwb Tsai, Cheng-Fa verfasserin aut A new and efficient ant-based heuristic method for solving the traveling salesman problem Oxford, UK Blackwell Publishing 2003 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Abstract: In this paper, we present an efficient metaheuristic approach for solving the problem of the traveling salesman. We introduce the multiple ant clans concept from parallel genetic algorithms to search solution space using different islands to avoid local minima in order to obtain a global minimum for solving the traveling salesman problem. Our simulation results indicate that the proposed novel traveling salesman problem method (called the ACOMAC algorithm) performs better than a promising approach named the ant colony system. This investigation is concerned with a real life logistics system design which optimizes the performance of a logistics system subject to a required service level in the vehicle routing problem. In this work, we also concentrate on developing a vehicle routing model by improving the ant colony system and using the multiple ant clans concept. The simulation results reveal that the proposed method is very effective and potentially useful in solving vehicle routing problems. 2003 Blackwell Publishing Journal Backfiles 1879-2005 |2003|||||||||| traveling salesman problem Tsai, Chun-Wei verfasserin aut Tseng, Ching-Chang verfasserin aut In Expert systems Oxford [u.a.] : Wiley-Blackwell, 1997 20(2003), 4, Seite 0 Online-Ressource (DE-627)NLEJ243925662 (DE-600)2016958-9 1468-0394 nnns volume:20 year:2003 number:4 pages:0 http://dx.doi.org/10.1111/1468-0394.00242 text/html Verlag Deutschlandweit zugänglich Volltext GBV_USEFLAG_U ZDB-1-DJB GBV_NL_ARTICLE AR 20 2003 4 0 |
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10.1111/1468-0394.00242 doi (DE-627)NLEJ24237512X DE-627 ger DE-627 rakwb Tsai, Cheng-Fa verfasserin aut A new and efficient ant-based heuristic method for solving the traveling salesman problem Oxford, UK Blackwell Publishing 2003 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Abstract: In this paper, we present an efficient metaheuristic approach for solving the problem of the traveling salesman. We introduce the multiple ant clans concept from parallel genetic algorithms to search solution space using different islands to avoid local minima in order to obtain a global minimum for solving the traveling salesman problem. Our simulation results indicate that the proposed novel traveling salesman problem method (called the ACOMAC algorithm) performs better than a promising approach named the ant colony system. This investigation is concerned with a real life logistics system design which optimizes the performance of a logistics system subject to a required service level in the vehicle routing problem. In this work, we also concentrate on developing a vehicle routing model by improving the ant colony system and using the multiple ant clans concept. The simulation results reveal that the proposed method is very effective and potentially useful in solving vehicle routing problems. 2003 Blackwell Publishing Journal Backfiles 1879-2005 |2003|||||||||| traveling salesman problem Tsai, Chun-Wei verfasserin aut Tseng, Ching-Chang verfasserin aut In Expert systems Oxford [u.a.] : Wiley-Blackwell, 1997 20(2003), 4, Seite 0 Online-Ressource (DE-627)NLEJ243925662 (DE-600)2016958-9 1468-0394 nnns volume:20 year:2003 number:4 pages:0 http://dx.doi.org/10.1111/1468-0394.00242 text/html Verlag Deutschlandweit zugänglich Volltext GBV_USEFLAG_U ZDB-1-DJB GBV_NL_ARTICLE AR 20 2003 4 0 |
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10.1111/1468-0394.00242 doi (DE-627)NLEJ24237512X DE-627 ger DE-627 rakwb Tsai, Cheng-Fa verfasserin aut A new and efficient ant-based heuristic method for solving the traveling salesman problem Oxford, UK Blackwell Publishing 2003 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Abstract: In this paper, we present an efficient metaheuristic approach for solving the problem of the traveling salesman. We introduce the multiple ant clans concept from parallel genetic algorithms to search solution space using different islands to avoid local minima in order to obtain a global minimum for solving the traveling salesman problem. Our simulation results indicate that the proposed novel traveling salesman problem method (called the ACOMAC algorithm) performs better than a promising approach named the ant colony system. This investigation is concerned with a real life logistics system design which optimizes the performance of a logistics system subject to a required service level in the vehicle routing problem. In this work, we also concentrate on developing a vehicle routing model by improving the ant colony system and using the multiple ant clans concept. The simulation results reveal that the proposed method is very effective and potentially useful in solving vehicle routing problems. 2003 Blackwell Publishing Journal Backfiles 1879-2005 |2003|||||||||| traveling salesman problem Tsai, Chun-Wei verfasserin aut Tseng, Ching-Chang verfasserin aut In Expert systems Oxford [u.a.] : Wiley-Blackwell, 1997 20(2003), 4, Seite 0 Online-Ressource (DE-627)NLEJ243925662 (DE-600)2016958-9 1468-0394 nnns volume:20 year:2003 number:4 pages:0 http://dx.doi.org/10.1111/1468-0394.00242 text/html Verlag Deutschlandweit zugänglich Volltext GBV_USEFLAG_U ZDB-1-DJB GBV_NL_ARTICLE AR 20 2003 4 0 |
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Abstract: In this paper, we present an efficient metaheuristic approach for solving the problem of the traveling salesman. We introduce the multiple ant clans concept from parallel genetic algorithms to search solution space using different islands to avoid local minima in order to obtain a global minimum for solving the traveling salesman problem. Our simulation results indicate that the proposed novel traveling salesman problem method (called the ACOMAC algorithm) performs better than a promising approach named the ant colony system. This investigation is concerned with a real life logistics system design which optimizes the performance of a logistics system subject to a required service level in the vehicle routing problem. In this work, we also concentrate on developing a vehicle routing model by improving the ant colony system and using the multiple ant clans concept. The simulation results reveal that the proposed method is very effective and potentially useful in solving vehicle routing problems. |
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Abstract: In this paper, we present an efficient metaheuristic approach for solving the problem of the traveling salesman. We introduce the multiple ant clans concept from parallel genetic algorithms to search solution space using different islands to avoid local minima in order to obtain a global minimum for solving the traveling salesman problem. Our simulation results indicate that the proposed novel traveling salesman problem method (called the ACOMAC algorithm) performs better than a promising approach named the ant colony system. This investigation is concerned with a real life logistics system design which optimizes the performance of a logistics system subject to a required service level in the vehicle routing problem. In this work, we also concentrate on developing a vehicle routing model by improving the ant colony system and using the multiple ant clans concept. The simulation results reveal that the proposed method is very effective and potentially useful in solving vehicle routing problems. |
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Abstract: In this paper, we present an efficient metaheuristic approach for solving the problem of the traveling salesman. We introduce the multiple ant clans concept from parallel genetic algorithms to search solution space using different islands to avoid local minima in order to obtain a global minimum for solving the traveling salesman problem. Our simulation results indicate that the proposed novel traveling salesman problem method (called the ACOMAC algorithm) performs better than a promising approach named the ant colony system. This investigation is concerned with a real life logistics system design which optimizes the performance of a logistics system subject to a required service level in the vehicle routing problem. In this work, we also concentrate on developing a vehicle routing model by improving the ant colony system and using the multiple ant clans concept. The simulation results reveal that the proposed method is very effective and potentially useful in solving vehicle routing problems. |
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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">NLEJ24237512X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20210707154133.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">120427s2003 xx |||||o 00| ||und c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1111/1468-0394.00242</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)NLEJ24237512X</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="100" ind1="1" ind2=" "><subfield code="a">Tsai, Cheng-Fa</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A new and efficient ant-based heuristic method for solving the traveling salesman problem</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Oxford, UK</subfield><subfield code="b">Blackwell Publishing</subfield><subfield code="c">2003</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">Abstract: In this paper, we present an efficient metaheuristic approach for solving the problem of the traveling salesman. We introduce the multiple ant clans concept from parallel genetic algorithms to search solution space using different islands to avoid local minima in order to obtain a global minimum for solving the traveling salesman problem. Our simulation results indicate that the proposed novel traveling salesman problem method (called the ACOMAC algorithm) performs better than a promising approach named the ant colony system. This investigation is concerned with a real life logistics system design which optimizes the performance of a logistics system subject to a required service level in the vehicle routing problem. In this work, we also concentrate on developing a vehicle routing model by improving the ant colony system and using the multiple ant clans concept. The simulation results reveal that the proposed method is very effective and potentially useful in solving vehicle routing problems.</subfield></datafield><datafield tag="533" ind1=" " ind2=" "><subfield code="d">2003</subfield><subfield code="f">Blackwell Publishing Journal Backfiles 1879-2005</subfield><subfield code="7">|2003||||||||||</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">traveling salesman problem</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tsai, Chun-Wei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tseng, Ching-Chang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Expert systems</subfield><subfield code="d">Oxford [u.a.] : Wiley-Blackwell, 1997</subfield><subfield code="g">20(2003), 4, Seite 0</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)NLEJ243925662</subfield><subfield code="w">(DE-600)2016958-9</subfield><subfield code="x">1468-0394</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:20</subfield><subfield code="g">year:2003</subfield><subfield code="g">number:4</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://dx.doi.org/10.1111/1468-0394.00242</subfield><subfield code="q">text/html</subfield><subfield code="x">Verlag</subfield><subfield code="z">Deutschlandweit zugänglich</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-1-DJB</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_NL_ARTICLE</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">20</subfield><subfield code="j">2003</subfield><subfield code="e">4</subfield><subfield code="h">0</subfield></datafield></record></collection>
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