Abstract:For the premature convergence and low efficiency optimization of the existing public transit vehicle dispatching algorithm, this paper puts forward a quantum particle swarm optimization algorithm with weight adaptive adjustments to construct optimal dispatching model aiming at the minimum cost and the shortest passenger s' mean waiting time. Firstly, the concept of focusing distance changing rate was introduced in this algorithm and inertial weighting factor was formulated as a function of focusing distance rate so as to provide the algorithm with effective dynamic adaptability. Meanwhile, a method of effective judgment of premature and stagnation is embedded in the algorithm. The optimization results show that this algorithm can effectively solve public transit vehicle dispatching problems.