Ant Colony Optimization
A discrete particle swarm optimization algorithm for the generalized traveling salesman problem
Proceedings of the 9th annual conference on Genetic and evolutionary computation
ParamILS: an automatic algorithm configuration framework
Journal of Artificial Intelligence Research
A memetic algorithm for the generalized traveling salesman problem
Natural Computing: an international journal
Computers and Operations Research
Effective neighborhood structures for the generalized traveling salesman problem
EvoCOP'08 Proceedings of the 8th European conference on Evolutionary computation in combinatorial optimization
Consultant-guided search: a new metaheuristic for combinatorial optimization problems
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Consultant-guided search algorithms with local search for the traveling salesman problem
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Consultant-guided search algorithms for the quadratic assignment problem
HM'10 Proceedings of the 7th international conference on Hybrid metaheuristics
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The generalized traveling salesman problem (GTSP) is an NP-hard problem that extends the classical traveling salesman problem by partitioning the nodes into clusters and looking for a minimum Hamiltonian tour visiting exactly one node from each cluster. In this paper, we combine the consultant-guided search technique with a local-global approach in order to solve efficiently the generalized traveling salesman problem. We use candidate lists in order to reduce the search space and we introduce efficient variants of 2-opt and 3-opt local search in order to improve the solutions. The resulting algorithm is applied to Euclidean GTSP instances derived from the TSPLIB library. The experimental results show that our algorithm is able to compete with the best existing algorithms in terms of solution quality and running time.