城市交通系统是一个庞大的系统，具有极强的随机性以及复杂性，要想实现对其有效控制，必须不断进行研究 和分析。本文主要讨论两种智能优化算法在城市交通控制应用中的对比。两种智能算法分别是混沌遗传算法和混沌粒子群 算法。通过对这两种智能优化算法的计算结果进行仿真发现，这两种算法的自适应性、鲁棒性以及自学习性都是相当强的，能 够有效地实现对地区交通信号的控制优化，并且由于固定周期控制方式在其中的应用，能够使车辆的平均延误情况得到有效 的缓解，对改善地区交通有着积极的意义。

Urban traffic system is a huge system, with strong randomness and complexity. In order to effectively control the system, we must continue to study and analyze. This paper mainly discusses on the application of two kinds of intelligent optimization algorithms in urban traffic control. The algorithms are chaos genetic algorithm and chaotic particle swarm optimization algorithm. Through the simulation of these two intelligent optimization algorithms, the results show that the adaptive robust and self-learning ability of these algorithms are very strong. They can realize the optimization of traffic signal effectively. Due to the application of fixed cycle control, the average delay of vehicle has been effectively alleviated, having positive significance to improve the traffic situation.