Dynamic Shortest Path Algorithm in Stochastic Traffic Networks Using PSO Based on Fluid Neural Network

• 作者: Hengqing Tong Yanfang Deng
• 来源期刊: 智能学习系统与应用(英文)
• 学科: 工学
• 关键词: Particle SWARM Optimization FLUID NEURON Network Shortest PATH TRAFFIC Networks
• 年，卷（期）: 2011,（1）
• 页码范围: 11-16
• 页数: 6页
• 分类号: TP39

摘要

The shortest path planning issure is critical for dynamic traffic assignment and route guidance in intelligent transportation systems. In this paper, a Particle Swarm Optimization (PSO) algorithm with priority-based encoding scheme based on fluid neural network (FNN) to search for the shortest path in stochastic traffic networks is introduced. The proposed algorithm overcomes the weight coefficient symmetry restrictions of the traditional FNN and disadvantage of easily getting into a local optimum for PSO. Simulation experiments have been carried out on different traffic network topologies consisting of 15-65 nodes and the results showed that the proposed approach can find the optimal path and closer sub-optimal paths with good success ratio. At the same time, the algorithms greatly improve the convergence efficiency of fluid neuron network.