Abstract: In order to overcome the drawback of basic PSO，such as being subject to falling into local optimization and being poor in performance of precision，a Bidirectional Parallel Catastrophic-Particle Swarm Optimization（BPC-PSO） has been developed through introducing bidirectional parallel tactics and a cusp catastrophic model in panicle swarm optimization algorithm，and the algorithm has been effectively used in dealing with the optimization of signal timing to urban traffic.Simulation data shows that the new method proposed in this paper is feasible and efficient.

Key words: Bidirectional Parallel Catastrophic-Particle Swarm Optimization（BPC-PSO）, urban traffic signal control, cusp catastrophic model, signal timing optimization, PI value

摘要： 针对基本粒子群优化算法易陷入局部极值点、搜索精度低等缺点，引入灾变模型，采用双向并行策略，提出一种双向并行灾变粒子群优化算法（BPC-PSO），并将其成功应用于城市区域交通控制信号参数配时优化。仿真结果表明：双向并行灾变粒子群算法相对于基本粒子群算法大大提高了寻找全局最优解的能力，使车辆平均延误和平均停车率都比基本粒子群算法有明显地降低。

关键词: 双向并行灾变粒子群优化算法, 城市区域交通控制, 尖点灾变模型, 信号优化配时, PI值