Computers and Operations Research
Swarm intelligence
A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem
Computers and Operations Research
The dynamic programming method in the generalized traveling salesman problem
Mathematical and Computer Modelling: An International Journal
A novel hybrid discrete differential evolution algorithm for blocking flow shop scheduling problems
Computers and Operations Research
An Efficient Approach to Web Service Selection
WISM '09 Proceedings of the International Conference on Web Information Systems and Mining
A memetic algorithm for the generalized traveling salesman problem
Natural Computing: an international journal
Solving ring loading problems using bio-inspired algorithms
Journal of Network and Computer Applications
Research on Web service selection based on cooperative evolution
Expert Systems with Applications: An International Journal
A hybrid heuristic approach for solving the generalized traveling salesman problem
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Solving vehicle assignment problem using evolutionary computation
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
Swarm optimisation algorithms applied to large balanced communication networks
Journal of Network and Computer Applications
A mixed-discrete Particle Swarm Optimization algorithm with explicit diversity-preservation
Structural and Multidisciplinary Optimization
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Dividing the set of nodes into clusters in the well-known traveling salesman problem results in the generalized traveling salesman problem which seeking a tour with minimum cost passing through only a single node from each cluster. In this paper, a discrete particle swarm optimization is presented to solve the problem on a set of benchmark instances. The discrete particle swarm optimization algorithm exploits the basic features of its continuous counterpart. It is also hybridized with a local search, variable neighborhood descend algorithm, to further improve the solution quality. In addition, some speed-up methods for greedy node insertions are presented. The discrete particle swarm optimization algorithm is tested on a set of benchmark instances with symmetric distances up to 442 nodes from the literature. Computational results show that the discrete particle optimization algorithm is very promising to solve the generalized traveling salesman problem.