A weighted-sum method for solving the bi-objective traveling thief problem
Many real-world optimization problems have multiple interacting components. Each of these can be an NP -hard problem, and they can be in conflict with each other, i.e., the optimal solution for one component does not necessarily represent an optimal solution for the other components. This can be a c...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Chagas, Jonatas B.C. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Influence of CeO - Aboutaleb, Wael A. ELSEVIER, 2021, an international journal, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:138 ; year:2022 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.cor.2021.105560 |
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| 520 | |a Many real-world optimization problems have multiple interacting components. Each of these can be an NP -hard problem, and they can be in conflict with each other, i.e., the optimal solution for one component does not necessarily represent an optimal solution for the other components. This can be a challenge for single-objective formulations, where the respective influence that each component has on the overall solution quality can vary from instance to instance. In this paper, we study a bi-objective formulation of the traveling thief problem, which has as components the traveling salesperson problem and the knapsack problem. We present a weighted-sum method that makes use of randomized versions of existing heuristics, that outperforms participants on 6 of 9 instances of recent competitions, and that has found new best solutions to 379 single-objective problem instances. | ||
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10.1016/j.cor.2021.105560 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001604.pica (DE-627)ELV056045077 (ELSEVIER)S0305-0548(21)00292-6 DE-627 ger DE-627 rakwb eng 540 530 VZ ASIEN DE-1a fid 6,25 ssgn 35.90 bkl 33.61 bkl 51.00 bkl Chagas, Jonatas B.C. verfasserin aut A weighted-sum method for solving the bi-objective traveling thief problem 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Many real-world optimization problems have multiple interacting components. Each of these can be an NP -hard problem, and they can be in conflict with each other, i.e., the optimal solution for one component does not necessarily represent an optimal solution for the other components. This can be a challenge for single-objective formulations, where the respective influence that each component has on the overall solution quality can vary from instance to instance. In this paper, we study a bi-objective formulation of the traveling thief problem, which has as components the traveling salesperson problem and the knapsack problem. We present a weighted-sum method that makes use of randomized versions of existing heuristics, that outperforms participants on 6 of 9 instances of recent competitions, and that has found new best solutions to 379 single-objective problem instances. Many real-world optimization problems have multiple interacting components. Each of these can be an NP -hard problem, and they can be in conflict with each other, i.e., the optimal solution for one component does not necessarily represent an optimal solution for the other components. This can be a challenge for single-objective formulations, where the respective influence that each component has on the overall solution quality can vary from instance to instance. In this paper, we study a bi-objective formulation of the traveling thief problem, which has as components the traveling salesperson problem and the knapsack problem. We present a weighted-sum method that makes use of randomized versions of existing heuristics, that outperforms participants on 6 of 9 instances of recent competitions, and that has found new best solutions to 379 single-objective problem instances. Multi-component problems Elsevier Bi-objective formulations Elsevier Traveling salesperson problem Elsevier Knapsack problem Elsevier Wagner, Markus oth Enthalten in Elsevier Aboutaleb, Wael A. ELSEVIER Influence of CeO 2021 an international journal Amsterdam [u.a.] (DE-627)ELV00698584X volume:138 year:2022 pages:0 https://doi.org/10.1016/j.cor.2021.105560 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-ASIEN SSG-OLC-PHA 35.90 Festkörperchemie VZ 33.61 Festkörperphysik VZ 51.00 Werkstoffkunde: Allgemeines VZ AR 138 2022 0 |
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10.1016/j.cor.2021.105560 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001604.pica (DE-627)ELV056045077 (ELSEVIER)S0305-0548(21)00292-6 DE-627 ger DE-627 rakwb eng 540 530 VZ ASIEN DE-1a fid 6,25 ssgn 35.90 bkl 33.61 bkl 51.00 bkl Chagas, Jonatas B.C. verfasserin aut A weighted-sum method for solving the bi-objective traveling thief problem 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Many real-world optimization problems have multiple interacting components. Each of these can be an NP -hard problem, and they can be in conflict with each other, i.e., the optimal solution for one component does not necessarily represent an optimal solution for the other components. This can be a challenge for single-objective formulations, where the respective influence that each component has on the overall solution quality can vary from instance to instance. In this paper, we study a bi-objective formulation of the traveling thief problem, which has as components the traveling salesperson problem and the knapsack problem. We present a weighted-sum method that makes use of randomized versions of existing heuristics, that outperforms participants on 6 of 9 instances of recent competitions, and that has found new best solutions to 379 single-objective problem instances. Many real-world optimization problems have multiple interacting components. Each of these can be an NP -hard problem, and they can be in conflict with each other, i.e., the optimal solution for one component does not necessarily represent an optimal solution for the other components. This can be a challenge for single-objective formulations, where the respective influence that each component has on the overall solution quality can vary from instance to instance. In this paper, we study a bi-objective formulation of the traveling thief problem, which has as components the traveling salesperson problem and the knapsack problem. We present a weighted-sum method that makes use of randomized versions of existing heuristics, that outperforms participants on 6 of 9 instances of recent competitions, and that has found new best solutions to 379 single-objective problem instances. Multi-component problems Elsevier Bi-objective formulations Elsevier Traveling salesperson problem Elsevier Knapsack problem Elsevier Wagner, Markus oth Enthalten in Elsevier Aboutaleb, Wael A. ELSEVIER Influence of CeO 2021 an international journal Amsterdam [u.a.] (DE-627)ELV00698584X volume:138 year:2022 pages:0 https://doi.org/10.1016/j.cor.2021.105560 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-ASIEN SSG-OLC-PHA 35.90 Festkörperchemie VZ 33.61 Festkörperphysik VZ 51.00 Werkstoffkunde: Allgemeines VZ AR 138 2022 0 |
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10.1016/j.cor.2021.105560 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001604.pica (DE-627)ELV056045077 (ELSEVIER)S0305-0548(21)00292-6 DE-627 ger DE-627 rakwb eng 540 530 VZ ASIEN DE-1a fid 6,25 ssgn 35.90 bkl 33.61 bkl 51.00 bkl Chagas, Jonatas B.C. verfasserin aut A weighted-sum method for solving the bi-objective traveling thief problem 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Many real-world optimization problems have multiple interacting components. Each of these can be an NP -hard problem, and they can be in conflict with each other, i.e., the optimal solution for one component does not necessarily represent an optimal solution for the other components. This can be a challenge for single-objective formulations, where the respective influence that each component has on the overall solution quality can vary from instance to instance. In this paper, we study a bi-objective formulation of the traveling thief problem, which has as components the traveling salesperson problem and the knapsack problem. We present a weighted-sum method that makes use of randomized versions of existing heuristics, that outperforms participants on 6 of 9 instances of recent competitions, and that has found new best solutions to 379 single-objective problem instances. Many real-world optimization problems have multiple interacting components. Each of these can be an NP -hard problem, and they can be in conflict with each other, i.e., the optimal solution for one component does not necessarily represent an optimal solution for the other components. This can be a challenge for single-objective formulations, where the respective influence that each component has on the overall solution quality can vary from instance to instance. In this paper, we study a bi-objective formulation of the traveling thief problem, which has as components the traveling salesperson problem and the knapsack problem. We present a weighted-sum method that makes use of randomized versions of existing heuristics, that outperforms participants on 6 of 9 instances of recent competitions, and that has found new best solutions to 379 single-objective problem instances. Multi-component problems Elsevier Bi-objective formulations Elsevier Traveling salesperson problem Elsevier Knapsack problem Elsevier Wagner, Markus oth Enthalten in Elsevier Aboutaleb, Wael A. ELSEVIER Influence of CeO 2021 an international journal Amsterdam [u.a.] (DE-627)ELV00698584X volume:138 year:2022 pages:0 https://doi.org/10.1016/j.cor.2021.105560 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-ASIEN SSG-OLC-PHA 35.90 Festkörperchemie VZ 33.61 Festkörperphysik VZ 51.00 Werkstoffkunde: Allgemeines VZ AR 138 2022 0 |
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Chagas, Jonatas B.C. |
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Chagas, Jonatas B.C. ddc 540 fid ASIEN ssgn 6,25 bkl 35.90 bkl 33.61 bkl 51.00 Elsevier Multi-component problems Elsevier Bi-objective formulations Elsevier Traveling salesperson problem Elsevier Knapsack problem A weighted-sum method for solving the bi-objective traveling thief problem |
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a weighted-sum method for solving the bi-objective traveling thief problem |
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A weighted-sum method for solving the bi-objective traveling thief problem |
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Many real-world optimization problems have multiple interacting components. Each of these can be an NP -hard problem, and they can be in conflict with each other, i.e., the optimal solution for one component does not necessarily represent an optimal solution for the other components. This can be a challenge for single-objective formulations, where the respective influence that each component has on the overall solution quality can vary from instance to instance. In this paper, we study a bi-objective formulation of the traveling thief problem, which has as components the traveling salesperson problem and the knapsack problem. We present a weighted-sum method that makes use of randomized versions of existing heuristics, that outperforms participants on 6 of 9 instances of recent competitions, and that has found new best solutions to 379 single-objective problem instances. |
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Many real-world optimization problems have multiple interacting components. Each of these can be an NP -hard problem, and they can be in conflict with each other, i.e., the optimal solution for one component does not necessarily represent an optimal solution for the other components. This can be a challenge for single-objective formulations, where the respective influence that each component has on the overall solution quality can vary from instance to instance. In this paper, we study a bi-objective formulation of the traveling thief problem, which has as components the traveling salesperson problem and the knapsack problem. We present a weighted-sum method that makes use of randomized versions of existing heuristics, that outperforms participants on 6 of 9 instances of recent competitions, and that has found new best solutions to 379 single-objective problem instances. |
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Many real-world optimization problems have multiple interacting components. Each of these can be an NP -hard problem, and they can be in conflict with each other, i.e., the optimal solution for one component does not necessarily represent an optimal solution for the other components. This can be a challenge for single-objective formulations, where the respective influence that each component has on the overall solution quality can vary from instance to instance. In this paper, we study a bi-objective formulation of the traveling thief problem, which has as components the traveling salesperson problem and the knapsack problem. We present a weighted-sum method that makes use of randomized versions of existing heuristics, that outperforms participants on 6 of 9 instances of recent competitions, and that has found new best solutions to 379 single-objective problem instances. |
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A weighted-sum method for solving the bi-objective traveling thief problem |
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