A non-dominated sorting based customized random-key genetic algorithm for the bi-objective traveling thief problem
Abstract In this paper, we propose a method to solve a bi-objective variant of the well-studied traveling thief problem (TTP). The TTP is a multi-component problem that combines two classic combinatorial problems: traveling salesman problem and knapsack problem. We address the BI-TTP, a bi-objective...
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| Autor*in: |
Chagas, Jonatas B. C. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2020 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of heuristics - Springer US, 1995, 27(2020), 3 vom: 20. Sept., Seite 267-301 |
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| Übergeordnetes Werk: |
volume:27 ; year:2020 ; number:3 ; day:20 ; month:09 ; pages:267-301 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10732-020-09457-7 |
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| Katalog-ID: |
OLC2125592460 |
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| 520 | |a Abstract In this paper, we propose a method to solve a bi-objective variant of the well-studied traveling thief problem (TTP). The TTP is a multi-component problem that combines two classic combinatorial problems: traveling salesman problem and knapsack problem. We address the BI-TTP, a bi-objective version of the TTP, where the goal is to minimize the overall traveling time and to maximize the profit of the collected items. Our proposed method is based on a biased-random key genetic algorithm with customizations addressing problem-specific characteristics. We incorporate domain knowledge through a combination of near-optimal solutions of each subproblem in the initial population and use a custom repair operator to avoid the evaluation of infeasible solutions. The bi-objective aspect of the problem is addressed through an elite population extracted based on the non-dominated rank and crowding distance. Furthermore, we provide a comprehensive study showing the influence of each parameter on the performance. Finally, we discuss the results of the BI-TTP competitions at EMO-2019 and GECCO-2019 conferences where our method has won first and second places, respectively, thus proving its ability to find high-quality solutions consistently. | ||
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10.1007/s10732-020-09457-7 doi (DE-627)OLC2125592460 (DE-He213)s10732-020-09457-7-p DE-627 ger DE-627 rakwb eng 510 VZ 3,2 24 ssgn Chagas, Jonatas B. C. verfasserin (orcid)0000-0001-7965-8419 aut A non-dominated sorting based customized random-key genetic algorithm for the bi-objective traveling thief problem 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract In this paper, we propose a method to solve a bi-objective variant of the well-studied traveling thief problem (TTP). The TTP is a multi-component problem that combines two classic combinatorial problems: traveling salesman problem and knapsack problem. We address the BI-TTP, a bi-objective version of the TTP, where the goal is to minimize the overall traveling time and to maximize the profit of the collected items. Our proposed method is based on a biased-random key genetic algorithm with customizations addressing problem-specific characteristics. We incorporate domain knowledge through a combination of near-optimal solutions of each subproblem in the initial population and use a custom repair operator to avoid the evaluation of infeasible solutions. The bi-objective aspect of the problem is addressed through an elite population extracted based on the non-dominated rank and crowding distance. Furthermore, we provide a comprehensive study showing the influence of each parameter on the performance. Finally, we discuss the results of the BI-TTP competitions at EMO-2019 and GECCO-2019 conferences where our method has won first and second places, respectively, thus proving its ability to find high-quality solutions consistently. Combinatorial optimization Multi-objective optimization Real-world optimization problem Traveling thief problem NSGA-II Blank, Julian (orcid)0000-0002-2227-6476 aut Wagner, Markus (orcid)0000-0002-3124-0061 aut Souza, Marcone J. F. (orcid)0000-0002-7141-357X aut Deb, Kalyanmoy (orcid)0000-0001-7402-9939 aut Enthalten in Journal of heuristics Springer US, 1995 27(2020), 3 vom: 20. Sept., Seite 267-301 (DE-627)215140281 (DE-600)1333974-6 (DE-576)063244721 1381-1231 nnns volume:27 year:2020 number:3 day:20 month:09 pages:267-301 https://doi.org/10.1007/s10732-020-09457-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_2108 AR 27 2020 3 20 09 267-301 |
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10.1007/s10732-020-09457-7 doi (DE-627)OLC2125592460 (DE-He213)s10732-020-09457-7-p DE-627 ger DE-627 rakwb eng 510 VZ 3,2 24 ssgn Chagas, Jonatas B. C. verfasserin (orcid)0000-0001-7965-8419 aut A non-dominated sorting based customized random-key genetic algorithm for the bi-objective traveling thief problem 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract In this paper, we propose a method to solve a bi-objective variant of the well-studied traveling thief problem (TTP). The TTP is a multi-component problem that combines two classic combinatorial problems: traveling salesman problem and knapsack problem. We address the BI-TTP, a bi-objective version of the TTP, where the goal is to minimize the overall traveling time and to maximize the profit of the collected items. Our proposed method is based on a biased-random key genetic algorithm with customizations addressing problem-specific characteristics. We incorporate domain knowledge through a combination of near-optimal solutions of each subproblem in the initial population and use a custom repair operator to avoid the evaluation of infeasible solutions. The bi-objective aspect of the problem is addressed through an elite population extracted based on the non-dominated rank and crowding distance. Furthermore, we provide a comprehensive study showing the influence of each parameter on the performance. Finally, we discuss the results of the BI-TTP competitions at EMO-2019 and GECCO-2019 conferences where our method has won first and second places, respectively, thus proving its ability to find high-quality solutions consistently. Combinatorial optimization Multi-objective optimization Real-world optimization problem Traveling thief problem NSGA-II Blank, Julian (orcid)0000-0002-2227-6476 aut Wagner, Markus (orcid)0000-0002-3124-0061 aut Souza, Marcone J. F. (orcid)0000-0002-7141-357X aut Deb, Kalyanmoy (orcid)0000-0001-7402-9939 aut Enthalten in Journal of heuristics Springer US, 1995 27(2020), 3 vom: 20. Sept., Seite 267-301 (DE-627)215140281 (DE-600)1333974-6 (DE-576)063244721 1381-1231 nnns volume:27 year:2020 number:3 day:20 month:09 pages:267-301 https://doi.org/10.1007/s10732-020-09457-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_2108 AR 27 2020 3 20 09 267-301 |
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10.1007/s10732-020-09457-7 doi (DE-627)OLC2125592460 (DE-He213)s10732-020-09457-7-p DE-627 ger DE-627 rakwb eng 510 VZ 3,2 24 ssgn Chagas, Jonatas B. C. verfasserin (orcid)0000-0001-7965-8419 aut A non-dominated sorting based customized random-key genetic algorithm for the bi-objective traveling thief problem 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract In this paper, we propose a method to solve a bi-objective variant of the well-studied traveling thief problem (TTP). The TTP is a multi-component problem that combines two classic combinatorial problems: traveling salesman problem and knapsack problem. We address the BI-TTP, a bi-objective version of the TTP, where the goal is to minimize the overall traveling time and to maximize the profit of the collected items. Our proposed method is based on a biased-random key genetic algorithm with customizations addressing problem-specific characteristics. We incorporate domain knowledge through a combination of near-optimal solutions of each subproblem in the initial population and use a custom repair operator to avoid the evaluation of infeasible solutions. The bi-objective aspect of the problem is addressed through an elite population extracted based on the non-dominated rank and crowding distance. Furthermore, we provide a comprehensive study showing the influence of each parameter on the performance. Finally, we discuss the results of the BI-TTP competitions at EMO-2019 and GECCO-2019 conferences where our method has won first and second places, respectively, thus proving its ability to find high-quality solutions consistently. Combinatorial optimization Multi-objective optimization Real-world optimization problem Traveling thief problem NSGA-II Blank, Julian (orcid)0000-0002-2227-6476 aut Wagner, Markus (orcid)0000-0002-3124-0061 aut Souza, Marcone J. F. (orcid)0000-0002-7141-357X aut Deb, Kalyanmoy (orcid)0000-0001-7402-9939 aut Enthalten in Journal of heuristics Springer US, 1995 27(2020), 3 vom: 20. Sept., Seite 267-301 (DE-627)215140281 (DE-600)1333974-6 (DE-576)063244721 1381-1231 nnns volume:27 year:2020 number:3 day:20 month:09 pages:267-301 https://doi.org/10.1007/s10732-020-09457-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_2108 AR 27 2020 3 20 09 267-301 |
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10.1007/s10732-020-09457-7 doi (DE-627)OLC2125592460 (DE-He213)s10732-020-09457-7-p DE-627 ger DE-627 rakwb eng 510 VZ 3,2 24 ssgn Chagas, Jonatas B. C. verfasserin (orcid)0000-0001-7965-8419 aut A non-dominated sorting based customized random-key genetic algorithm for the bi-objective traveling thief problem 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract In this paper, we propose a method to solve a bi-objective variant of the well-studied traveling thief problem (TTP). The TTP is a multi-component problem that combines two classic combinatorial problems: traveling salesman problem and knapsack problem. We address the BI-TTP, a bi-objective version of the TTP, where the goal is to minimize the overall traveling time and to maximize the profit of the collected items. Our proposed method is based on a biased-random key genetic algorithm with customizations addressing problem-specific characteristics. We incorporate domain knowledge through a combination of near-optimal solutions of each subproblem in the initial population and use a custom repair operator to avoid the evaluation of infeasible solutions. The bi-objective aspect of the problem is addressed through an elite population extracted based on the non-dominated rank and crowding distance. Furthermore, we provide a comprehensive study showing the influence of each parameter on the performance. Finally, we discuss the results of the BI-TTP competitions at EMO-2019 and GECCO-2019 conferences where our method has won first and second places, respectively, thus proving its ability to find high-quality solutions consistently. Combinatorial optimization Multi-objective optimization Real-world optimization problem Traveling thief problem NSGA-II Blank, Julian (orcid)0000-0002-2227-6476 aut Wagner, Markus (orcid)0000-0002-3124-0061 aut Souza, Marcone J. F. (orcid)0000-0002-7141-357X aut Deb, Kalyanmoy (orcid)0000-0001-7402-9939 aut Enthalten in Journal of heuristics Springer US, 1995 27(2020), 3 vom: 20. Sept., Seite 267-301 (DE-627)215140281 (DE-600)1333974-6 (DE-576)063244721 1381-1231 nnns volume:27 year:2020 number:3 day:20 month:09 pages:267-301 https://doi.org/10.1007/s10732-020-09457-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_2108 AR 27 2020 3 20 09 267-301 |
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A non-dominated sorting based customized random-key genetic algorithm for the bi-objective traveling thief problem |
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A non-dominated sorting based customized random-key genetic algorithm for the bi-objective traveling thief problem |
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Chagas, Jonatas B. C. |
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Chagas, Jonatas B. C. Blank, Julian Wagner, Markus Souza, Marcone J. F. Deb, Kalyanmoy |
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a non-dominated sorting based customized random-key genetic algorithm for the bi-objective traveling thief problem |
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A non-dominated sorting based customized random-key genetic algorithm for the bi-objective traveling thief problem |
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Abstract In this paper, we propose a method to solve a bi-objective variant of the well-studied traveling thief problem (TTP). The TTP is a multi-component problem that combines two classic combinatorial problems: traveling salesman problem and knapsack problem. We address the BI-TTP, a bi-objective version of the TTP, where the goal is to minimize the overall traveling time and to maximize the profit of the collected items. Our proposed method is based on a biased-random key genetic algorithm with customizations addressing problem-specific characteristics. We incorporate domain knowledge through a combination of near-optimal solutions of each subproblem in the initial population and use a custom repair operator to avoid the evaluation of infeasible solutions. The bi-objective aspect of the problem is addressed through an elite population extracted based on the non-dominated rank and crowding distance. Furthermore, we provide a comprehensive study showing the influence of each parameter on the performance. Finally, we discuss the results of the BI-TTP competitions at EMO-2019 and GECCO-2019 conferences where our method has won first and second places, respectively, thus proving its ability to find high-quality solutions consistently. © Springer Science+Business Media, LLC, part of Springer Nature 2020 |
| abstractGer |
Abstract In this paper, we propose a method to solve a bi-objective variant of the well-studied traveling thief problem (TTP). The TTP is a multi-component problem that combines two classic combinatorial problems: traveling salesman problem and knapsack problem. We address the BI-TTP, a bi-objective version of the TTP, where the goal is to minimize the overall traveling time and to maximize the profit of the collected items. Our proposed method is based on a biased-random key genetic algorithm with customizations addressing problem-specific characteristics. We incorporate domain knowledge through a combination of near-optimal solutions of each subproblem in the initial population and use a custom repair operator to avoid the evaluation of infeasible solutions. The bi-objective aspect of the problem is addressed through an elite population extracted based on the non-dominated rank and crowding distance. Furthermore, we provide a comprehensive study showing the influence of each parameter on the performance. Finally, we discuss the results of the BI-TTP competitions at EMO-2019 and GECCO-2019 conferences where our method has won first and second places, respectively, thus proving its ability to find high-quality solutions consistently. © Springer Science+Business Media, LLC, part of Springer Nature 2020 |
| abstract_unstemmed |
Abstract In this paper, we propose a method to solve a bi-objective variant of the well-studied traveling thief problem (TTP). The TTP is a multi-component problem that combines two classic combinatorial problems: traveling salesman problem and knapsack problem. We address the BI-TTP, a bi-objective version of the TTP, where the goal is to minimize the overall traveling time and to maximize the profit of the collected items. Our proposed method is based on a biased-random key genetic algorithm with customizations addressing problem-specific characteristics. We incorporate domain knowledge through a combination of near-optimal solutions of each subproblem in the initial population and use a custom repair operator to avoid the evaluation of infeasible solutions. The bi-objective aspect of the problem is addressed through an elite population extracted based on the non-dominated rank and crowding distance. Furthermore, we provide a comprehensive study showing the influence of each parameter on the performance. Finally, we discuss the results of the BI-TTP competitions at EMO-2019 and GECCO-2019 conferences where our method has won first and second places, respectively, thus proving its ability to find high-quality solutions consistently. © Springer Science+Business Media, LLC, part of Springer Nature 2020 |
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A non-dominated sorting based customized random-key genetic algorithm for the bi-objective traveling thief problem |
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