Author(s)

Chunyan Qiu, Xi Yang And Yang Liu

Corresponding Author

Yang Liu

Abstract

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.

Keywords

Vehicle Routing, Optimization, Particle Swarm Algorithm