Distribution center location problems and vehicle routing problem are two very important constituent of logistics distribution system. Particle swarm algorithm is an uncomplicated principle, application extensive optimization algorithm, which was improved to be applied distribution center location problems and vehicle routing problem, and has important theoretical significance and practical value.Firstly gives a comprehensive introduction to distribution center location problem and vehicle routing problem and that to the principle of the particle swarm optimization algorithm and parameter selection strategy make more comprehensive interpretation. Combined with the simulated annealing algorithm the global search ability strong advantage, this paper puts forward the particle swarm optimization algorithm fusing simulated annealing thought and using standard test function to make experiment at the same time compare and analysis the improved PSO and basic particle swarm optimization algorithm, is used to test the effect and performance whose is superior. The experimental results show that the improved particle swarm optimization algorithm is used to jump out of local optimal solution ability stronger, avoid the local optimal more effectively, convergence speed and accuracy of convergence has been improved. The improved algorithm applies to the distribution center location problems and vehicle routing problem, set simulation experiment, and achieved good simulation effect. The results show that the improved particle swarm algorithm has a high ratio of success in dealing with complex combinatorial optimization problem solving such problems -core NP more effectively. |