Adaptive particle swarm optimization based on population entropy for MDVRPTW

Multi-depots vehicle routing problem with time windows (MDVRPTW) is a kind of NP combination problem which possesses important practical value. In order to overcome PSO's premature convergence and slow astringe, an adaptive particle swarm optimization based on population entropy is put forward, it uses the population entropy to makes a quantitative description about the diversity of population, in the meanwhile cellular is introduced to PSO, and adaptively adjusts the cellular structure according to the change of population entropy to have an effective balance between the global exploration and local exploitation, so enhance the performance of the algorithm. In this paper, the algorithm is used to solve MDVRPTW, a kind of new particles coding method is constructed and the solution algorithm is developed. The simulation results of example indicate that the algorithm has better capability of jumping out of local optimum than GA and PSO.