摘要
离散交通信号控制模型在自适应粒子群算法中引入变异算子,以更新粒子群算法的个体极值点和全局极值点。在此模型基础上,应用四种自适应变异粒子群算法优化城市交通信号控制配时方案,同时比较分析各变异算子的优劣。然后选出最优的自适应变异粒子群算法对不同的交通流进行连续优化控制。仿真表明该混合算法可解决易陷入局部收敛的缺陷并能够有效实现交通信号优化控制。
The mutation operator was introduced by control model of discrete traffic signal to update the best point of the individual and the best point of the global in adaptive-Mutation particle swarm optimization algorithm （AMPSOA）. Based on model, the four adaptive-mutation particle swarm optimization algorithms can be applied to optimize the timing plan of the traffic signal control; at the same time, the quality of every mutation operator is compared and analyzed. Then the optimum adaptive-mutation particle swarm optimization algorithms are chosen to carry out the optimum control on different traffic stream. Simulation results show the hybrid method solves the defect of the local convergence and demonstrate the effectiveness of the AMPSOA, which can achieve the optimal control of in the traffic signal.
出处
《兵工自动化》
2006年第6期66-68,71,共4页
Ordnance Industry Automation
关键词
粒子群优化算法
实数编码遗传算法
变异
交通信号控制
Swarm optimization algorithm
Real-code genetic algorithm
Mutation
Traffic signal control
作者简介
付绍昌（1972-），男，湖南人，1993年毕业于中南大学，现湘潭大学在读硕士，从事计算机控制与应用、智能交通控制研究。
