Ant colony optimization with local search for dynamic travelling salesman problems
For a dynamic travelling salesman problem, the weights (or travelling times) between two cities (or nodes) may be subject to changes. Ant colony optimization (ACO) algorithms have proved to be powerful methods to tackle such problems due to their adaptation capabilities. It has been shown that the integration of local search operators can significantly improve the performance of ACO. In this paper, a memetic ACO algorithm, where a local search operator (called unstring and string) is integrated into ACO, is proposed to address dynamic travelling salesman problems. The best solution from ACO is passed to the local search operator, which removes and inserts cities in such a way that improves the solution quality. The proposed memetic ACO algorithm is designed to address both symmetric and asymmetric dynamic travelling salesman problems. The experimental results show the efficiency of the proposed memetic algorithm for solving dynamic travelling salesman problems in comparison with other state-of-the-art algorithms.
Citation : Mavrovouniotis, M., Muller, F.M. and Yang, S. (2016) Ant colony optimization with local search for dynamic travelling salesman problems. IEEE Transactions on Cybernetics, in press, 2016.
Research Group : Centre for Computational Intelligence
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes