TY - GEN
T1 - Solving the traveling thief problem using orchestration in optimization networks
AU - Karder, Johannes
AU - Beham, Andreas
AU - Wagner, Stefan
AU - Affenzeller, Michael
N1 - Publisher Copyright:
© Springer International Publishing AG 2018.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018
Y1 - 2018
N2 - Optimization problems can sometimes be divided into multiple subproblems. Working on these subproblems instead of the actual master problem can have some advantages, e.g. if they are standard problems, it is possible to use already existing algorithms, whereas specialized algorithms would have to be implemented for the master problem. In this paper we approach the NP-hard Traveling Thief Problem by implementing different cooperative approaches using optimization networks. Orchestration is used to guide the algorithms that solve the respective subproblems. We conduct experiments on some instances of a larger benchmark set to compare the different network approaches to best known results, as well as a sophisticated, monolithic approach. Using optimization networks, we are able to find new best solutions for all of the selected problem instances.
AB - Optimization problems can sometimes be divided into multiple subproblems. Working on these subproblems instead of the actual master problem can have some advantages, e.g. if they are standard problems, it is possible to use already existing algorithms, whereas specialized algorithms would have to be implemented for the master problem. In this paper we approach the NP-hard Traveling Thief Problem by implementing different cooperative approaches using optimization networks. Orchestration is used to guide the algorithms that solve the respective subproblems. We conduct experiments on some instances of a larger benchmark set to compare the different network approaches to best known results, as well as a sophisticated, monolithic approach. Using optimization networks, we are able to find new best solutions for all of the selected problem instances.
KW - Evolutionary algorithm
KW - HeuristicLab
KW - Metaheuristic
KW - Optimization network
KW - Traveling thief problem
UR - http://www.scopus.com/inward/record.url?scp=85041805852&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-74718-7_37
DO - 10.1007/978-3-319-74718-7_37
M3 - Conference contribution
SN - 9783319747170
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 307
EP - 315
BT - Computer Aided Systems Theory – EUROCAST 2017 - 16th International Conference, Revised Selected Papers
A2 - Moreno-Diaz, Roberto
A2 - Quesada-Arencibia, Alexis
A2 - Pichler, Franz
PB - Springer
T2 - 16th International Conference on Computer Aided Systems Theory, EUROCAST 2017
Y2 - 19 February 2017 through 24 February 2017
ER -