Algorithm selection for the team orienteering problem

This work utilizes Algorithm Selection for solving the Team Orienteering Problem (TOP). The TOP is an NP-hard combinatorial optimization problem in the routing domain. This problem has been modelled with various extensions to address different real-world problems like tourist trip planning. The comp...

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Main Authors: MISIR, Mustafa, GUNAWAN, Aldy, VANSTEENWEGEN, Pieter
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Language:English
Published: Institutional Knowledge at Singapore Management University 2022
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Online Access:https://ink.library.smu.edu.sg/sis_research/7159
https://ink.library.smu.edu.sg/context/sis_research/article/8162/viewcontent/Evolutionary_Computation_in_Combinatorial_Optimization.pdf
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spelling sg-smu-ink.sis_research-81622022-05-12T04:26:00Z Algorithm selection for the team orienteering problem MISIR, Mustafa GUNAWAN, Aldy VANSTEENWEGEN, Pieter This work utilizes Algorithm Selection for solving the Team Orienteering Problem (TOP). The TOP is an NP-hard combinatorial optimization problem in the routing domain. This problem has been modelled with various extensions to address different real-world problems like tourist trip planning. The complexity of the problem motivated to devise new algorithms. However, none of the existing algorithms came with the best performance across all the widely used benchmark instances. This fact suggests that there is a performance gap to fill. This gap can be targeted by developing more new algorithms as attempted by many researchers before. An alternative strategy is performing Algorithm Selection that will automatically choose the most appropriate algorithm for a given problem instance. This study considers the existing algorithms for the Team Orienteering Problem as the candidate method set. For matching the best algorithm with each problem instance, the specific instance characteristics are used as the instance features. An algorithm Selection approach, namely ALORS, is used to conduct the selection mission. The computational analysis based on 157 instances showed that Algorithm Selection outperforms the state-of-the-art algorithms despite the simplicity of the Algorithm Selection setting. Further analysis illustrates the match between certain algorithms and certain instances. Additional analysis showed that the time budget significantly affects the algorithms’ performance. 2022-04-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/7159 info:doi/10.1007/978-3-031-04148-8_3 https://ink.library.smu.edu.sg/context/sis_research/article/8162/viewcontent/Evolutionary_Computation_in_Combinatorial_Optimization.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Artificial Intelligence and Robotics Theory and Algorithms
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Artificial Intelligence and Robotics
Theory and Algorithms
spellingShingle Artificial Intelligence and Robotics
Theory and Algorithms
MISIR, Mustafa
GUNAWAN, Aldy
VANSTEENWEGEN, Pieter
Algorithm selection for the team orienteering problem
description This work utilizes Algorithm Selection for solving the Team Orienteering Problem (TOP). The TOP is an NP-hard combinatorial optimization problem in the routing domain. This problem has been modelled with various extensions to address different real-world problems like tourist trip planning. The complexity of the problem motivated to devise new algorithms. However, none of the existing algorithms came with the best performance across all the widely used benchmark instances. This fact suggests that there is a performance gap to fill. This gap can be targeted by developing more new algorithms as attempted by many researchers before. An alternative strategy is performing Algorithm Selection that will automatically choose the most appropriate algorithm for a given problem instance. This study considers the existing algorithms for the Team Orienteering Problem as the candidate method set. For matching the best algorithm with each problem instance, the specific instance characteristics are used as the instance features. An algorithm Selection approach, namely ALORS, is used to conduct the selection mission. The computational analysis based on 157 instances showed that Algorithm Selection outperforms the state-of-the-art algorithms despite the simplicity of the Algorithm Selection setting. Further analysis illustrates the match between certain algorithms and certain instances. Additional analysis showed that the time budget significantly affects the algorithms’ performance.
format text
author MISIR, Mustafa
GUNAWAN, Aldy
VANSTEENWEGEN, Pieter
author_facet MISIR, Mustafa
GUNAWAN, Aldy
VANSTEENWEGEN, Pieter
author_sort MISIR, Mustafa
title Algorithm selection for the team orienteering problem
title_short Algorithm selection for the team orienteering problem
title_full Algorithm selection for the team orienteering problem
title_fullStr Algorithm selection for the team orienteering problem
title_full_unstemmed Algorithm selection for the team orienteering problem
title_sort algorithm selection for the team orienteering problem
publisher Institutional Knowledge at Singapore Management University
publishDate 2022
url https://ink.library.smu.edu.sg/sis_research/7159
https://ink.library.smu.edu.sg/context/sis_research/article/8162/viewcontent/Evolutionary_Computation_in_Combinatorial_Optimization.pdf
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