Particle Swarm Optimization Combined with Ant Colony Optimization for the Multiple Traveling Salesman Problem
A lot of practical problem, such as the scheduling of jobs on multiple parallel production lines and the scheduling of multiple vehicles transporting goods in logistics, can be modeled as the multiple traveling salesman problem (MTSP). Due to the combinatorial complexity of the MTSP, it is necessary to use heuristics to solve the problem, and a discrete particle swarm optimization (DPSO) algorithm is employed in this paper. Particle swarm optimization (PSO) in the continuous space has obtained great success in resolving some minimization problems. But when applying PSO for the MTSP, a difficulty rises, which is to find a suitable mapping between sequence and continuous position of particles in particle swarm optimization. For overcoming this difficulty, PSO is combined with ant colony optimization (ACO), and the mapping between sequence and continuous position of particles is established. To verify the efficiency of the DPSO algorithm, it is used to solve the MTSP and its performance is compared with the ACO and some traditional DPSO algorithms. The computational results show that the proposed DPSO algorithm is efficient.
Dongming Guo, Jun Wang, Zhenyuan Jia, Renke Kang, Hang Gao, and Xuyue Wang
H. K. Feng et al., "Particle Swarm Optimization Combined with Ant Colony Optimization for the Multiple Traveling Salesman Problem", Materials Science Forum, Vols. 626-627, pp. 717-722, 2009