A novel List-Constrained Randomized VND approach in GPU for the Traveling Thief Problem
The Traveling Thief Problem (TTP) is a multi-component combinatorial optimization problem that combines two well-known problems in the literature: the Traveling Salesman Problem (TSP) and the Knapsack Problem (KP). This paper proposes a novel list-constrained local search process inspired in Variabl...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Araujo, Rodolfo Pereira [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018transfer abstract |
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| Schlagwörter: |
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| Umfang: |
8 |
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| Übergeordnetes Werk: |
Enthalten in: Magnetic properties of the Ni–Cu–Zn system doped with magnesium oxide - Hemeda, O.M. ELSEVIER, 2015transfer abstract, Amsterdam |
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| Übergeordnetes Werk: |
volume:66 ; year:2018 ; pages:183-190 ; extent:8 |
| Links: |
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| DOI / URN: |
10.1016/j.endm.2018.03.024 |
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ELV042674050 |
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| 520 | |a The Traveling Thief Problem (TTP) is a multi-component combinatorial optimization problem that combines two well-known problems in the literature: the Traveling Salesman Problem (TSP) and the Knapsack Problem (KP). This paper proposes a novel list-constrained local search process inspired in Variable Neighborhood Descent (VND) for multiple neighborhood structures, combined with a metaheuristic Greedy Randomized Adaptive Search Procedure (GRASP). The local search implementation was made in a Graphics Processing Unit (GPU) architecture in order to explore the massive number of computing cores to simultaneously explore neighbor solutions, while the GRASP was implemented exploring the natural parallelism of a multi-core CPU. The computational results were compared to state-of-the-art results in literature and indicate promising research directions for the design of novel search algorithms in high performance architectures. | ||
| 520 | |a The Traveling Thief Problem (TTP) is a multi-component combinatorial optimization problem that combines two well-known problems in the literature: the Traveling Salesman Problem (TSP) and the Knapsack Problem (KP). This paper proposes a novel list-constrained local search process inspired in Variable Neighborhood Descent (VND) for multiple neighborhood structures, combined with a metaheuristic Greedy Randomized Adaptive Search Procedure (GRASP). The local search implementation was made in a Graphics Processing Unit (GPU) architecture in order to explore the massive number of computing cores to simultaneously explore neighbor solutions, while the GRASP was implemented exploring the natural parallelism of a multi-core CPU. The computational results were compared to state-of-the-art results in literature and indicate promising research directions for the design of novel search algorithms in high performance architectures. | ||
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10.1016/j.endm.2018.03.024 doi GBV00000000000198A.pica (DE-627)ELV042674050 (ELSEVIER)S1571-0653(18)30070-2 DE-627 ger DE-627 rakwb eng 510 510 DE-600 530 VZ 610 VZ 44.90 bkl Araujo, Rodolfo Pereira verfasserin aut A novel List-Constrained Randomized VND approach in GPU for the Traveling Thief Problem 2018transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The Traveling Thief Problem (TTP) is a multi-component combinatorial optimization problem that combines two well-known problems in the literature: the Traveling Salesman Problem (TSP) and the Knapsack Problem (KP). This paper proposes a novel list-constrained local search process inspired in Variable Neighborhood Descent (VND) for multiple neighborhood structures, combined with a metaheuristic Greedy Randomized Adaptive Search Procedure (GRASP). The local search implementation was made in a Graphics Processing Unit (GPU) architecture in order to explore the massive number of computing cores to simultaneously explore neighbor solutions, while the GRASP was implemented exploring the natural parallelism of a multi-core CPU. The computational results were compared to state-of-the-art results in literature and indicate promising research directions for the design of novel search algorithms in high performance architectures. The Traveling Thief Problem (TTP) is a multi-component combinatorial optimization problem that combines two well-known problems in the literature: the Traveling Salesman Problem (TSP) and the Knapsack Problem (KP). This paper proposes a novel list-constrained local search process inspired in Variable Neighborhood Descent (VND) for multiple neighborhood structures, combined with a metaheuristic Greedy Randomized Adaptive Search Procedure (GRASP). The local search implementation was made in a Graphics Processing Unit (GPU) architecture in order to explore the massive number of computing cores to simultaneously explore neighbor solutions, while the GRASP was implemented exploring the natural parallelism of a multi-core CPU. The computational results were compared to state-of-the-art results in literature and indicate promising research directions for the design of novel search algorithms in high performance architectures. Traveling Thief Problem Elsevier Variable Neighborhood Descent Elsevier Graphics Processing Unit Elsevier Local Search Elsevier GRASP Elsevier Rios, Eyder oth Coelho, Igor Machado oth Marzulo, Leandro A.J. oth Clicia Castro, Maria oth Enthalten in Elsevier Science Hemeda, O.M. ELSEVIER Magnetic properties of the Ni–Cu–Zn system doped with magnesium oxide 2015transfer abstract Amsterdam (DE-627)ELV012993891 volume:66 year:2018 pages:183-190 extent:8 https://doi.org/10.1016/j.endm.2018.03.024 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 GBV_ILN_70 44.90 Neurologie VZ AR 66 2018 183-190 8 045F 510 |
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10.1016/j.endm.2018.03.024 doi GBV00000000000198A.pica (DE-627)ELV042674050 (ELSEVIER)S1571-0653(18)30070-2 DE-627 ger DE-627 rakwb eng 510 510 DE-600 530 VZ 610 VZ 44.90 bkl Araujo, Rodolfo Pereira verfasserin aut A novel List-Constrained Randomized VND approach in GPU for the Traveling Thief Problem 2018transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The Traveling Thief Problem (TTP) is a multi-component combinatorial optimization problem that combines two well-known problems in the literature: the Traveling Salesman Problem (TSP) and the Knapsack Problem (KP). This paper proposes a novel list-constrained local search process inspired in Variable Neighborhood Descent (VND) for multiple neighborhood structures, combined with a metaheuristic Greedy Randomized Adaptive Search Procedure (GRASP). The local search implementation was made in a Graphics Processing Unit (GPU) architecture in order to explore the massive number of computing cores to simultaneously explore neighbor solutions, while the GRASP was implemented exploring the natural parallelism of a multi-core CPU. The computational results were compared to state-of-the-art results in literature and indicate promising research directions for the design of novel search algorithms in high performance architectures. The Traveling Thief Problem (TTP) is a multi-component combinatorial optimization problem that combines two well-known problems in the literature: the Traveling Salesman Problem (TSP) and the Knapsack Problem (KP). This paper proposes a novel list-constrained local search process inspired in Variable Neighborhood Descent (VND) for multiple neighborhood structures, combined with a metaheuristic Greedy Randomized Adaptive Search Procedure (GRASP). The local search implementation was made in a Graphics Processing Unit (GPU) architecture in order to explore the massive number of computing cores to simultaneously explore neighbor solutions, while the GRASP was implemented exploring the natural parallelism of a multi-core CPU. The computational results were compared to state-of-the-art results in literature and indicate promising research directions for the design of novel search algorithms in high performance architectures. Traveling Thief Problem Elsevier Variable Neighborhood Descent Elsevier Graphics Processing Unit Elsevier Local Search Elsevier GRASP Elsevier Rios, Eyder oth Coelho, Igor Machado oth Marzulo, Leandro A.J. oth Clicia Castro, Maria oth Enthalten in Elsevier Science Hemeda, O.M. ELSEVIER Magnetic properties of the Ni–Cu–Zn system doped with magnesium oxide 2015transfer abstract Amsterdam (DE-627)ELV012993891 volume:66 year:2018 pages:183-190 extent:8 https://doi.org/10.1016/j.endm.2018.03.024 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 GBV_ILN_70 44.90 Neurologie VZ AR 66 2018 183-190 8 045F 510 |
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10.1016/j.endm.2018.03.024 doi GBV00000000000198A.pica (DE-627)ELV042674050 (ELSEVIER)S1571-0653(18)30070-2 DE-627 ger DE-627 rakwb eng 510 510 DE-600 530 VZ 610 VZ 44.90 bkl Araujo, Rodolfo Pereira verfasserin aut A novel List-Constrained Randomized VND approach in GPU for the Traveling Thief Problem 2018transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The Traveling Thief Problem (TTP) is a multi-component combinatorial optimization problem that combines two well-known problems in the literature: the Traveling Salesman Problem (TSP) and the Knapsack Problem (KP). This paper proposes a novel list-constrained local search process inspired in Variable Neighborhood Descent (VND) for multiple neighborhood structures, combined with a metaheuristic Greedy Randomized Adaptive Search Procedure (GRASP). The local search implementation was made in a Graphics Processing Unit (GPU) architecture in order to explore the massive number of computing cores to simultaneously explore neighbor solutions, while the GRASP was implemented exploring the natural parallelism of a multi-core CPU. The computational results were compared to state-of-the-art results in literature and indicate promising research directions for the design of novel search algorithms in high performance architectures. The Traveling Thief Problem (TTP) is a multi-component combinatorial optimization problem that combines two well-known problems in the literature: the Traveling Salesman Problem (TSP) and the Knapsack Problem (KP). This paper proposes a novel list-constrained local search process inspired in Variable Neighborhood Descent (VND) for multiple neighborhood structures, combined with a metaheuristic Greedy Randomized Adaptive Search Procedure (GRASP). The local search implementation was made in a Graphics Processing Unit (GPU) architecture in order to explore the massive number of computing cores to simultaneously explore neighbor solutions, while the GRASP was implemented exploring the natural parallelism of a multi-core CPU. The computational results were compared to state-of-the-art results in literature and indicate promising research directions for the design of novel search algorithms in high performance architectures. Traveling Thief Problem Elsevier Variable Neighborhood Descent Elsevier Graphics Processing Unit Elsevier Local Search Elsevier GRASP Elsevier Rios, Eyder oth Coelho, Igor Machado oth Marzulo, Leandro A.J. oth Clicia Castro, Maria oth Enthalten in Elsevier Science Hemeda, O.M. ELSEVIER Magnetic properties of the Ni–Cu–Zn system doped with magnesium oxide 2015transfer abstract Amsterdam (DE-627)ELV012993891 volume:66 year:2018 pages:183-190 extent:8 https://doi.org/10.1016/j.endm.2018.03.024 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 GBV_ILN_70 44.90 Neurologie VZ AR 66 2018 183-190 8 045F 510 |
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10.1016/j.endm.2018.03.024 doi GBV00000000000198A.pica (DE-627)ELV042674050 (ELSEVIER)S1571-0653(18)30070-2 DE-627 ger DE-627 rakwb eng 510 510 DE-600 530 VZ 610 VZ 44.90 bkl Araujo, Rodolfo Pereira verfasserin aut A novel List-Constrained Randomized VND approach in GPU for the Traveling Thief Problem 2018transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The Traveling Thief Problem (TTP) is a multi-component combinatorial optimization problem that combines two well-known problems in the literature: the Traveling Salesman Problem (TSP) and the Knapsack Problem (KP). This paper proposes a novel list-constrained local search process inspired in Variable Neighborhood Descent (VND) for multiple neighborhood structures, combined with a metaheuristic Greedy Randomized Adaptive Search Procedure (GRASP). The local search implementation was made in a Graphics Processing Unit (GPU) architecture in order to explore the massive number of computing cores to simultaneously explore neighbor solutions, while the GRASP was implemented exploring the natural parallelism of a multi-core CPU. The computational results were compared to state-of-the-art results in literature and indicate promising research directions for the design of novel search algorithms in high performance architectures. The Traveling Thief Problem (TTP) is a multi-component combinatorial optimization problem that combines two well-known problems in the literature: the Traveling Salesman Problem (TSP) and the Knapsack Problem (KP). This paper proposes a novel list-constrained local search process inspired in Variable Neighborhood Descent (VND) for multiple neighborhood structures, combined with a metaheuristic Greedy Randomized Adaptive Search Procedure (GRASP). The local search implementation was made in a Graphics Processing Unit (GPU) architecture in order to explore the massive number of computing cores to simultaneously explore neighbor solutions, while the GRASP was implemented exploring the natural parallelism of a multi-core CPU. The computational results were compared to state-of-the-art results in literature and indicate promising research directions for the design of novel search algorithms in high performance architectures. Traveling Thief Problem Elsevier Variable Neighborhood Descent Elsevier Graphics Processing Unit Elsevier Local Search Elsevier GRASP Elsevier Rios, Eyder oth Coelho, Igor Machado oth Marzulo, Leandro A.J. oth Clicia Castro, Maria oth Enthalten in Elsevier Science Hemeda, O.M. ELSEVIER Magnetic properties of the Ni–Cu–Zn system doped with magnesium oxide 2015transfer abstract Amsterdam (DE-627)ELV012993891 volume:66 year:2018 pages:183-190 extent:8 https://doi.org/10.1016/j.endm.2018.03.024 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 GBV_ILN_70 44.90 Neurologie VZ AR 66 2018 183-190 8 045F 510 |
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10.1016/j.endm.2018.03.024 doi GBV00000000000198A.pica (DE-627)ELV042674050 (ELSEVIER)S1571-0653(18)30070-2 DE-627 ger DE-627 rakwb eng 510 510 DE-600 530 VZ 610 VZ 44.90 bkl Araujo, Rodolfo Pereira verfasserin aut A novel List-Constrained Randomized VND approach in GPU for the Traveling Thief Problem 2018transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The Traveling Thief Problem (TTP) is a multi-component combinatorial optimization problem that combines two well-known problems in the literature: the Traveling Salesman Problem (TSP) and the Knapsack Problem (KP). This paper proposes a novel list-constrained local search process inspired in Variable Neighborhood Descent (VND) for multiple neighborhood structures, combined with a metaheuristic Greedy Randomized Adaptive Search Procedure (GRASP). The local search implementation was made in a Graphics Processing Unit (GPU) architecture in order to explore the massive number of computing cores to simultaneously explore neighbor solutions, while the GRASP was implemented exploring the natural parallelism of a multi-core CPU. The computational results were compared to state-of-the-art results in literature and indicate promising research directions for the design of novel search algorithms in high performance architectures. The Traveling Thief Problem (TTP) is a multi-component combinatorial optimization problem that combines two well-known problems in the literature: the Traveling Salesman Problem (TSP) and the Knapsack Problem (KP). This paper proposes a novel list-constrained local search process inspired in Variable Neighborhood Descent (VND) for multiple neighborhood structures, combined with a metaheuristic Greedy Randomized Adaptive Search Procedure (GRASP). The local search implementation was made in a Graphics Processing Unit (GPU) architecture in order to explore the massive number of computing cores to simultaneously explore neighbor solutions, while the GRASP was implemented exploring the natural parallelism of a multi-core CPU. The computational results were compared to state-of-the-art results in literature and indicate promising research directions for the design of novel search algorithms in high performance architectures. Traveling Thief Problem Elsevier Variable Neighborhood Descent Elsevier Graphics Processing Unit Elsevier Local Search Elsevier GRASP Elsevier Rios, Eyder oth Coelho, Igor Machado oth Marzulo, Leandro A.J. oth Clicia Castro, Maria oth Enthalten in Elsevier Science Hemeda, O.M. ELSEVIER Magnetic properties of the Ni–Cu–Zn system doped with magnesium oxide 2015transfer abstract Amsterdam (DE-627)ELV012993891 volume:66 year:2018 pages:183-190 extent:8 https://doi.org/10.1016/j.endm.2018.03.024 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 GBV_ILN_70 44.90 Neurologie VZ AR 66 2018 183-190 8 045F 510 |
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Magnetic properties of the Ni–Cu–Zn system doped with magnesium oxide |
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Araujo, Rodolfo Pereira @@aut@@ Rios, Eyder @@oth@@ Coelho, Igor Machado @@oth@@ Marzulo, Leandro A.J. @@oth@@ Clicia Castro, Maria @@oth@@ |
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A novel List-Constrained Randomized VND approach in GPU for the Traveling Thief Problem |
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The Traveling Thief Problem (TTP) is a multi-component combinatorial optimization problem that combines two well-known problems in the literature: the Traveling Salesman Problem (TSP) and the Knapsack Problem (KP). This paper proposes a novel list-constrained local search process inspired in Variable Neighborhood Descent (VND) for multiple neighborhood structures, combined with a metaheuristic Greedy Randomized Adaptive Search Procedure (GRASP). The local search implementation was made in a Graphics Processing Unit (GPU) architecture in order to explore the massive number of computing cores to simultaneously explore neighbor solutions, while the GRASP was implemented exploring the natural parallelism of a multi-core CPU. The computational results were compared to state-of-the-art results in literature and indicate promising research directions for the design of novel search algorithms in high performance architectures. |
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The Traveling Thief Problem (TTP) is a multi-component combinatorial optimization problem that combines two well-known problems in the literature: the Traveling Salesman Problem (TSP) and the Knapsack Problem (KP). This paper proposes a novel list-constrained local search process inspired in Variable Neighborhood Descent (VND) for multiple neighborhood structures, combined with a metaheuristic Greedy Randomized Adaptive Search Procedure (GRASP). The local search implementation was made in a Graphics Processing Unit (GPU) architecture in order to explore the massive number of computing cores to simultaneously explore neighbor solutions, while the GRASP was implemented exploring the natural parallelism of a multi-core CPU. The computational results were compared to state-of-the-art results in literature and indicate promising research directions for the design of novel search algorithms in high performance architectures. |
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The Traveling Thief Problem (TTP) is a multi-component combinatorial optimization problem that combines two well-known problems in the literature: the Traveling Salesman Problem (TSP) and the Knapsack Problem (KP). This paper proposes a novel list-constrained local search process inspired in Variable Neighborhood Descent (VND) for multiple neighborhood structures, combined with a metaheuristic Greedy Randomized Adaptive Search Procedure (GRASP). The local search implementation was made in a Graphics Processing Unit (GPU) architecture in order to explore the massive number of computing cores to simultaneously explore neighbor solutions, while the GRASP was implemented exploring the natural parallelism of a multi-core CPU. The computational results were compared to state-of-the-art results in literature and indicate promising research directions for the design of novel search algorithms in high performance architectures. |
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