Application of the improved particle swarm optimizer to vehicle routing and scheduling problems

Particle Swarm Optimizer (PSO) has several shortages when it is used for searching the best route of combinatorial optimization problems including vehicle routing and scheduling problems (VRSP), such as the premature convergence and easily limited to local optimal solution. The article proposed an improved PSO to overcome these shortcomings and improve its performance. The proposed algorithm integrates niche technology with the algorithm of PSO, and uses dynamic inertia weight to enhance its searching ability. In each iteration of the PSO, inertia weight is calculated to improve the searching ability at first, and then the local best positions are determined by niche technology, at last by demonstrating the power of this approach on a test case, the results derived from GA, ACO, PSO and the improved PSO are compared and analyzed in the experiment. It proved that the improved PSO is effective. The improved PSO has its significance to the general resource scheduling and can play a role in practice.