2019 International Conference on Advanced Manufacturing, Computation and Optimization
Vehicle Routing Problem Based on Improved Particle Swarm Optimization
Chunyan Qiu, Xi Yang and Yang Liu
The use of intelligent optimization algorithms to optimize vehicle routing problem has become a hot topic in international researches. The normal particle swarm optimization (PSO) algorithm is a validated evolutionary computation way of searching the extreme of function, which is simple in application and quick in convergence, but low in precision and easy in premature convergence. In this paper, the improved particle swarm optimization algorithm is used to optimize the logistics vehicle path by setting the inertia factor to 0. Through the simulation experiment analysis, the improved particle swarm optimization algorithm has better convergence (linear convergence). This algorithm avoids the problem that the global optimal is replaced by the local value, reduces the number of iterations required for search in optimal solution, and shortens the optimization time.
Vehicle Routing; Optimization; Particle Swarm Algorithm
Cite this paper:
Chunyan Qiu, Xi Yang and Yang Liu. Vehicle Routing Problem Based on Improved Particle Swarm Optimization. 2019 International Conference on Advanced Manufacturing, Computation and Optimization (AMCO 2019), 2019, Vol.1: 15-19. DOI: https://doi.org/10.35532/JCES.V1.003.