An ant colony algorithm for solving fixed destination multi-depot multiple traveling salesmen problems

Authors:
Soheil Ghafurian;Nikbakhsh Javadian
Affiliations:
Department of Industrial Engineering, Mazandaran University of Science and Technology, Iran;Department of Industrial Engineering, Mazandaran University of Science and Technology, Iran
Venue:
Applied Soft Computing
Year:
2011

Citing 4
Cited 5

Reactive tabu search in unmanned aerial reconnaissance simulations

Proceedings of the 30th conference on Winter simulation
Competition-based neural network for the multiple travelling salesmen problem with minmax objective

Computers and Operations Research - Special issue on the traveling salesman problem
Multiple criteria optimization based on unsupervised learning and fuzzy inference applied to the vehicle routing problem

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - SBRN'02
Ant system: optimization by a colony of cooperating agents

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Solving the traveling salesman problem based on an adaptive simulated annealing algorithm with greedy search

Applied Soft Computing
Inspection planning in the polygonal domain by Self-Organizing Map

Applied Soft Computing
Effect of a risk factor in convoy merging manoeuvres considering uncertainty in travelling times

Applied Soft Computing
A model induced max-min ant colony optimization for asymmetric traveling salesman problem

Applied Soft Computing
An improved meta-heuristic for makespan minimization of a single batch machine with non-identical job sizes

Computers and Operations Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

The fixed destination multi-depot multiple traveling salesmen problem (MmTSP) is a problem, in which more than one salesmen depart from several starting cities and having returned to the starting city, form tours so that each city is visited with exactly one salesman, and the tour lengths stay within certain limits. This problem is of a great complexity and few investigations have been done on it previously. In this paper an ant system is designed to solve the problem and the results are compared to the answers obtained by solving the same problems by Lingo 8.0 which uses exact methods.