Ants can solve the team orienteering problem |
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| Authors: | Liangjun Ke Claudia Archetti Zuren Feng |
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| Affiliation: | 1. State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an, China;2. Department of Quantitative Methods, University of Brescia, Brescia, Italy;1. Sorbonne universités, Université de Technologie de Compiègne, CNRS, Heudiasyc UMR 7253, CS 60 319, 60 203 Compiègne cedex;2. University of Nottingham, School of Computer Science, Jubilee Campus, Wollaton Road, Nottingham NG8 1BB, United Kingdom;3. Université Libanaise, École Doctorale des Sciences et de Technologie, Campus Hadath, Beyrouth, Liban;1. Department of Information Management, Chang Gung University, Taoyuan, Taiwan;2. Department of Industrial Engineering and Management, Ming Chi University of Technology, Taipei, Taiwan;3. Department of Neurology, Chang Gung Memorial Hospital, Linkou, Taoyuan, Taiwan;4. Department of Industrial Management, National Taiwan University of Science and Technology, Taipei, Taiwan;1. Ghent University, Department of Industrial Management, Belgium;2. University of Antwerp, Operations Research Group ANT/OR, Belgium;3. KU Leuven, Centre of Industrial Management, Traffic and Infrastructure, Belgium;1. TNO, The Netherlands;2. Netherlands Defence Academy, The Netherlands;3. Econometric Institute, Erasmus University Rotterdam, The Netherlands;4. Department of Operations Research, VU University Amsterdam, The Netherlands;1. Graduate Program in Systems Engineering, Universidad Autónoma de Nuevo León (UANL), Mexico;2. Département de mathématiques et de génie industriel, École Polytechnique e Montréal, Canada;3. Canada Research Chair in Distribution Management, HEC Montréal, Canada |
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| Abstract: | The team orienteering problem (TOP) involves finding a set of paths from the starting point to the ending point such that the total collected reward received from visiting a subset of locations is maximized and the length of each path is restricted by a pre-specified limit. In this paper, an ant colony optimization (ACO) approach is proposed for the team orienteering problem. Four methods, i.e., the sequential, deterministic-concurrent and random-concurrent and simultaneous methods, are proposed to construct candidate solutions in the framework of ACO. We compare these methods according to the results obtained on well-known problems from the literature. Finally, we compare the algorithm with several existing algorithms. The results show that our algorithm is promising. |
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