Road Network Matching Method Based on Particle Swarm Optimization Algorithm

Abstract

Combined the global optimization ability of particle swarm algorithm and memory capacity of tabu algorithm, this paper proposed an automatic vector road network matching method based on the combination of particle swarm optimization and tabu strategy.Firstly, the similarity between node entities is evaluated by means of geometric and topological characteristics.Then, the basic principle of global optimization of particle swarm optimization is introduced and road matching model based on particle swarm optimization algorithm is designed.Meanwhile, the tabu search algorithm is joined, by using the ability of tabu strategy which expanded the search of the neighborhood.The algorithm fully reflects the "climbing" feature of tabu strategy, in order to find the global optimal solution of the matching relationship of road network entities.Three different forms of road network data of Wuhan are selected to test our method, the result indicates that the matching method based on the combination of particle swarm optimization and tabu strategy is effective and feasible, which can provide a new idea to solve the matching problem.