A Team ant colony optimization algorithm for the multiple travelling salesmen problem with MinMax objective

Abstract

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.