Self-organizing maps
Competition-based neural network for the multiple travelling salesmen problem with minmax objective
Computers and Operations Research - Special issue on the traveling salesman problem
The Ant System Applied to the Quadratic Assignment Problem
IEEE Transactions on Knowledge and Data Engineering
Ant Colony Optimization
An Ant Colony Optimization Algorithm for Multiple Travelling Salesman Problem
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 1
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Tobacco distribution vehicle routing program and the resolving method
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
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In this paper, a Team ant colony optimization algorithm (TACO) is proposed for the multiple travelling salesman problem with MinMax objective. The novel idea is to replace every ant in an ant colony optimization algorithm, for example Ant Colony System [1], with a team of ants and letting those teams construct solutions to the multiple travelling salesman problem. The simulation results show that the proposed algorithm outperforms existing neural network based approaches in solution quality. Furthermore, the presented experiments demonstrate the feasibility of the proposed approach in multi-robot path planning.