A Logistic Distribution Routes Solving Strategy Based on the Physarum Network and Ant Colony Optimization Algorithm

The Physarum Network with single inlet and multi outlet model (SMPN) exhibits a unique feature that the critical pipelines are reserved with the evolution of network. In addition, ant colony optimization algorithm is a classic optimization algorithm of simulated evolutionary algorithms, which has been used to solve optimal scheduling problems. In this paper, drawing on this feature, an optimized Ant Colony Optimization (ACO) algorithm denoted as SMPNACO algorithm is proposed based on the Physarum Network and Ant Colony Optimization Algorithm (ACO) to solve the Vehicle Routing Problem (VRP). Throughout the algorithm, the amount of pheromone flowed in network are related to the customers' requirement. When the pheromone matrix is updated, the SMPNACO algorithm updates both the pheromone released by ants and the flowing pheromone in the Physarum Network. By adding extra pheromones in the Physarum Network improves the convergence performance of Ant Colony Optimization algorithm. The simulative experiments show that the SMPNACO algorithm is less affected by the initial total pheromone, this algorithm is feasible in solving the small scale VRP, and can effectively solve the VRP.