A two-phase ant colony optimization based approach for single depot multiple travelling salesman problem in Type-2 fuzzy environment
Abstract In this paper, a two-phase ant colony optimization (ACO) based approach has been presented to solve a single depot multiple travelling salesmen problem (mTSP) in Type-2 Gaussian fuzzy environment. In the single depot mTSP, a set of nodes and a set of salesmen are present, and each of the ci...
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Gespeichert in:
| Autor*in: |
Changdar, Chiranjit [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Nature B.V. 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Artificial intelligence review - Springer Netherlands, 1987, 56(2022), 2 vom: 02. Mai, Seite 965-993 |
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| Übergeordnetes Werk: |
volume:56 ; year:2022 ; number:2 ; day:02 ; month:05 ; pages:965-993 |
| Links: |
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| DOI / URN: |
10.1007/s10462-022-10190-9 |
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OLC2133645543 |
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| 520 | |a Abstract In this paper, a two-phase ant colony optimization (ACO) based approach has been presented to solve a single depot multiple travelling salesmen problem (mTSP) in Type-2 Gaussian fuzzy environment. In the single depot mTSP, a set of nodes and a set of salesmen are present, and each of the cities must be visited exactly once by the salesmen such that all of them start and finish at a depot. In this paper, a two-phase algorithm has been devised with ACO algorithm and some features of genetic algorithm (GA) to solve the projected problem. The devised algorithm is working appropriately with single depot mTSP. Here, in the first phase, the ACO algorithm is used for creating complete paths, and after that in the second phase, the GA features are used for optimizing the paths of multiple travellers. Moreover, the travelling cost is considered as Type-2 Gaussian fuzzy in nature and is reduced to its approximate crisp value using the reduction method of critical values. Some benchmark instances from TSPLIB have been used for performance analysis of the proposed algorithm. Computated results show that the devised algorithm is a competitive one for solving mTSP. Computational results with different datasets have been presented and some sensitivity analysis has also been done for fuzzy instances. | ||
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10.1007/s10462-022-10190-9 doi (DE-627)OLC2133645543 (DE-He213)s10462-022-10190-9-p DE-627 ger DE-627 rakwb eng 004 VZ 54.00 bkl Changdar, Chiranjit verfasserin aut A two-phase ant colony optimization based approach for single depot multiple travelling salesman problem in Type-2 fuzzy environment 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022 Abstract In this paper, a two-phase ant colony optimization (ACO) based approach has been presented to solve a single depot multiple travelling salesmen problem (mTSP) in Type-2 Gaussian fuzzy environment. In the single depot mTSP, a set of nodes and a set of salesmen are present, and each of the cities must be visited exactly once by the salesmen such that all of them start and finish at a depot. In this paper, a two-phase algorithm has been devised with ACO algorithm and some features of genetic algorithm (GA) to solve the projected problem. The devised algorithm is working appropriately with single depot mTSP. Here, in the first phase, the ACO algorithm is used for creating complete paths, and after that in the second phase, the GA features are used for optimizing the paths of multiple travellers. Moreover, the travelling cost is considered as Type-2 Gaussian fuzzy in nature and is reduced to its approximate crisp value using the reduction method of critical values. Some benchmark instances from TSPLIB have been used for performance analysis of the proposed algorithm. Computated results show that the devised algorithm is a competitive one for solving mTSP. Computational results with different datasets have been presented and some sensitivity analysis has also been done for fuzzy instances. Multiple travelling salesman problem Ant colony optimization Genetic algorithm Type-2 Gaussian fuzzy number Critical-value reduction Immigration Mondal, Moumita aut Giri, Pravash Kumar aut Nandi, Utpal aut Pal, Rajat Kumar aut Enthalten in Artificial intelligence review Springer Netherlands, 1987 56(2022), 2 vom: 02. Mai, Seite 965-993 (DE-627)129223018 (DE-600)56633-0 (DE-576)014458209 0269-2821 nnns volume:56 year:2022 number:2 day:02 month:05 pages:965-993 https://doi.org/10.1007/s10462-022-10190-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT 54.00 VZ AR 56 2022 2 02 05 965-993 |
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10.1007/s10462-022-10190-9 doi (DE-627)OLC2133645543 (DE-He213)s10462-022-10190-9-p DE-627 ger DE-627 rakwb eng 004 VZ 54.00 bkl Changdar, Chiranjit verfasserin aut A two-phase ant colony optimization based approach for single depot multiple travelling salesman problem in Type-2 fuzzy environment 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022 Abstract In this paper, a two-phase ant colony optimization (ACO) based approach has been presented to solve a single depot multiple travelling salesmen problem (mTSP) in Type-2 Gaussian fuzzy environment. In the single depot mTSP, a set of nodes and a set of salesmen are present, and each of the cities must be visited exactly once by the salesmen such that all of them start and finish at a depot. In this paper, a two-phase algorithm has been devised with ACO algorithm and some features of genetic algorithm (GA) to solve the projected problem. The devised algorithm is working appropriately with single depot mTSP. Here, in the first phase, the ACO algorithm is used for creating complete paths, and after that in the second phase, the GA features are used for optimizing the paths of multiple travellers. Moreover, the travelling cost is considered as Type-2 Gaussian fuzzy in nature and is reduced to its approximate crisp value using the reduction method of critical values. Some benchmark instances from TSPLIB have been used for performance analysis of the proposed algorithm. Computated results show that the devised algorithm is a competitive one for solving mTSP. Computational results with different datasets have been presented and some sensitivity analysis has also been done for fuzzy instances. Multiple travelling salesman problem Ant colony optimization Genetic algorithm Type-2 Gaussian fuzzy number Critical-value reduction Immigration Mondal, Moumita aut Giri, Pravash Kumar aut Nandi, Utpal aut Pal, Rajat Kumar aut Enthalten in Artificial intelligence review Springer Netherlands, 1987 56(2022), 2 vom: 02. Mai, Seite 965-993 (DE-627)129223018 (DE-600)56633-0 (DE-576)014458209 0269-2821 nnns volume:56 year:2022 number:2 day:02 month:05 pages:965-993 https://doi.org/10.1007/s10462-022-10190-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT 54.00 VZ AR 56 2022 2 02 05 965-993 |
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10.1007/s10462-022-10190-9 doi (DE-627)OLC2133645543 (DE-He213)s10462-022-10190-9-p DE-627 ger DE-627 rakwb eng 004 VZ 54.00 bkl Changdar, Chiranjit verfasserin aut A two-phase ant colony optimization based approach for single depot multiple travelling salesman problem in Type-2 fuzzy environment 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022 Abstract In this paper, a two-phase ant colony optimization (ACO) based approach has been presented to solve a single depot multiple travelling salesmen problem (mTSP) in Type-2 Gaussian fuzzy environment. In the single depot mTSP, a set of nodes and a set of salesmen are present, and each of the cities must be visited exactly once by the salesmen such that all of them start and finish at a depot. In this paper, a two-phase algorithm has been devised with ACO algorithm and some features of genetic algorithm (GA) to solve the projected problem. The devised algorithm is working appropriately with single depot mTSP. Here, in the first phase, the ACO algorithm is used for creating complete paths, and after that in the second phase, the GA features are used for optimizing the paths of multiple travellers. Moreover, the travelling cost is considered as Type-2 Gaussian fuzzy in nature and is reduced to its approximate crisp value using the reduction method of critical values. Some benchmark instances from TSPLIB have been used for performance analysis of the proposed algorithm. Computated results show that the devised algorithm is a competitive one for solving mTSP. Computational results with different datasets have been presented and some sensitivity analysis has also been done for fuzzy instances. Multiple travelling salesman problem Ant colony optimization Genetic algorithm Type-2 Gaussian fuzzy number Critical-value reduction Immigration Mondal, Moumita aut Giri, Pravash Kumar aut Nandi, Utpal aut Pal, Rajat Kumar aut Enthalten in Artificial intelligence review Springer Netherlands, 1987 56(2022), 2 vom: 02. Mai, Seite 965-993 (DE-627)129223018 (DE-600)56633-0 (DE-576)014458209 0269-2821 nnns volume:56 year:2022 number:2 day:02 month:05 pages:965-993 https://doi.org/10.1007/s10462-022-10190-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT 54.00 VZ AR 56 2022 2 02 05 965-993 |
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10.1007/s10462-022-10190-9 doi (DE-627)OLC2133645543 (DE-He213)s10462-022-10190-9-p DE-627 ger DE-627 rakwb eng 004 VZ 54.00 bkl Changdar, Chiranjit verfasserin aut A two-phase ant colony optimization based approach for single depot multiple travelling salesman problem in Type-2 fuzzy environment 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022 Abstract In this paper, a two-phase ant colony optimization (ACO) based approach has been presented to solve a single depot multiple travelling salesmen problem (mTSP) in Type-2 Gaussian fuzzy environment. In the single depot mTSP, a set of nodes and a set of salesmen are present, and each of the cities must be visited exactly once by the salesmen such that all of them start and finish at a depot. In this paper, a two-phase algorithm has been devised with ACO algorithm and some features of genetic algorithm (GA) to solve the projected problem. The devised algorithm is working appropriately with single depot mTSP. Here, in the first phase, the ACO algorithm is used for creating complete paths, and after that in the second phase, the GA features are used for optimizing the paths of multiple travellers. Moreover, the travelling cost is considered as Type-2 Gaussian fuzzy in nature and is reduced to its approximate crisp value using the reduction method of critical values. Some benchmark instances from TSPLIB have been used for performance analysis of the proposed algorithm. Computated results show that the devised algorithm is a competitive one for solving mTSP. Computational results with different datasets have been presented and some sensitivity analysis has also been done for fuzzy instances. Multiple travelling salesman problem Ant colony optimization Genetic algorithm Type-2 Gaussian fuzzy number Critical-value reduction Immigration Mondal, Moumita aut Giri, Pravash Kumar aut Nandi, Utpal aut Pal, Rajat Kumar aut Enthalten in Artificial intelligence review Springer Netherlands, 1987 56(2022), 2 vom: 02. Mai, Seite 965-993 (DE-627)129223018 (DE-600)56633-0 (DE-576)014458209 0269-2821 nnns volume:56 year:2022 number:2 day:02 month:05 pages:965-993 https://doi.org/10.1007/s10462-022-10190-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT 54.00 VZ AR 56 2022 2 02 05 965-993 |
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a two-phase ant colony optimization based approach for single depot multiple travelling salesman problem in type-2 fuzzy environment |
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A two-phase ant colony optimization based approach for single depot multiple travelling salesman problem in Type-2 fuzzy environment |
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Abstract In this paper, a two-phase ant colony optimization (ACO) based approach has been presented to solve a single depot multiple travelling salesmen problem (mTSP) in Type-2 Gaussian fuzzy environment. In the single depot mTSP, a set of nodes and a set of salesmen are present, and each of the cities must be visited exactly once by the salesmen such that all of them start and finish at a depot. In this paper, a two-phase algorithm has been devised with ACO algorithm and some features of genetic algorithm (GA) to solve the projected problem. The devised algorithm is working appropriately with single depot mTSP. Here, in the first phase, the ACO algorithm is used for creating complete paths, and after that in the second phase, the GA features are used for optimizing the paths of multiple travellers. Moreover, the travelling cost is considered as Type-2 Gaussian fuzzy in nature and is reduced to its approximate crisp value using the reduction method of critical values. Some benchmark instances from TSPLIB have been used for performance analysis of the proposed algorithm. Computated results show that the devised algorithm is a competitive one for solving mTSP. Computational results with different datasets have been presented and some sensitivity analysis has also been done for fuzzy instances. © The Author(s), under exclusive licence to Springer Nature B.V. 2022 |
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Abstract In this paper, a two-phase ant colony optimization (ACO) based approach has been presented to solve a single depot multiple travelling salesmen problem (mTSP) in Type-2 Gaussian fuzzy environment. In the single depot mTSP, a set of nodes and a set of salesmen are present, and each of the cities must be visited exactly once by the salesmen such that all of them start and finish at a depot. In this paper, a two-phase algorithm has been devised with ACO algorithm and some features of genetic algorithm (GA) to solve the projected problem. The devised algorithm is working appropriately with single depot mTSP. Here, in the first phase, the ACO algorithm is used for creating complete paths, and after that in the second phase, the GA features are used for optimizing the paths of multiple travellers. Moreover, the travelling cost is considered as Type-2 Gaussian fuzzy in nature and is reduced to its approximate crisp value using the reduction method of critical values. Some benchmark instances from TSPLIB have been used for performance analysis of the proposed algorithm. Computated results show that the devised algorithm is a competitive one for solving mTSP. Computational results with different datasets have been presented and some sensitivity analysis has also been done for fuzzy instances. © The Author(s), under exclusive licence to Springer Nature B.V. 2022 |
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Abstract In this paper, a two-phase ant colony optimization (ACO) based approach has been presented to solve a single depot multiple travelling salesmen problem (mTSP) in Type-2 Gaussian fuzzy environment. In the single depot mTSP, a set of nodes and a set of salesmen are present, and each of the cities must be visited exactly once by the salesmen such that all of them start and finish at a depot. In this paper, a two-phase algorithm has been devised with ACO algorithm and some features of genetic algorithm (GA) to solve the projected problem. The devised algorithm is working appropriately with single depot mTSP. Here, in the first phase, the ACO algorithm is used for creating complete paths, and after that in the second phase, the GA features are used for optimizing the paths of multiple travellers. Moreover, the travelling cost is considered as Type-2 Gaussian fuzzy in nature and is reduced to its approximate crisp value using the reduction method of critical values. Some benchmark instances from TSPLIB have been used for performance analysis of the proposed algorithm. Computated results show that the devised algorithm is a competitive one for solving mTSP. Computational results with different datasets have been presented and some sensitivity analysis has also been done for fuzzy instances. © The Author(s), under exclusive licence to Springer Nature B.V. 2022 |
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A two-phase ant colony optimization based approach for single depot multiple travelling salesman problem in Type-2 fuzzy environment |
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