Research on Particle Swarm Optimization Algorithm with Jump Factor for Groupage Cargo Scheduling Problem

Abstract

In order to efficiently plan the routing of dispatching vehicles and satisfy all customers' routing demands with as little expenditure as possible, a multi-depot joint routing of dispatching vehicles (MDJGVRP) mathematical model with unsplit demands was established, and a new neighborhood adaptive particle swarm optimization algorithm with jump factor was proposed to solve the problem. In the iterative process of the algorithm, the optimal value stagnation problem is solved by constantly adjusting the particle search neighborhood and dynamically updating the jump factor, so as to effectively improve the ability of the algorithm to jump out of the local optimal value. Compared with the original particle swarm algorithm, artificial bee colony algorithm and bat algorithm, the size calculation example is verified that the algorithm has faster convergence speed and optimization ability.