基于遗传算法的CBTC系统AP部署优化问题研究  

Research on Optimization of CBTC System AP DeploymentBased on Genetic Algorithm

作　　者：吴文艾 张治国[1] 罗永 尚丽莎 WU Wenai;ZHANG Zhiguo;LUO Yong;SHANG Lisha(Xi’an Traffic Engineering Institute,Xi’an 710300,China;China Railway First Bureau Group Electrical Engineering Co.Ltd,Xi’an 710038,Chian)

机构地区：[1]西安交通工程学院,西安710300 [2]中铁一局集团电务工程有限公司,西安710038

出　　处：《自动化与仪器仪表》2023年第3期125-129,133,共6页Automation & Instrumentation

基　　金：2022年西安交通工程学院中青年基金项目《基于CBTC系统的轨旁设备设置方法研究》(2022KY-01)。

摘　　要：为进一步提高CBTC系统的信息传输稳定性,基于遗传算法对CBTC系统的AP部署优化问题进行研究,以实现对AP部署的进一步优化。其中,AP部署的优化方法选择精确中断概率法;选择改进的遗传算法对AP部署优化的搜索方法进行优化。仿真结果表明,改进后的遗传算法能够实现更快的收敛,同时能够保持较低的相对误差和较快的运行速度。与未改进的遗传算法相比,改进的遗传算法的相对误差和运行速度分别为前者的0.125倍和100倍,验证了该算法的优越性。以上结果验证了本研究进行优化的可行性和必要性,具有一定的实际参考价值。In order to further improve the information transmission stability of CBTC system, the optimization of AP deployment in CBTC system is studied based on genetic algorithm to further optimize the AP deployment. Among them, the accurate outage probability method is selected as the optimization method of AP deployment;The improved genetic algorithm is selected to optimize the search method of AP deployment optimization. The simulation results show that the improved genetic algorithm can achieve faster convergence, while maintaining low relative error and faster running speed. Compared with the unimproved genetic algorithm, the relative error and running speed of the improved genetic algorithm are 0.125 times and 100 times of the former respectively, which verifies the superiority of the algorithm. The above results verify the feasibility and necessity of optimization in this study, and have a certain practical reference value.

关 键 词：CBTC系统 AP部署 遗传算法 精英保留策略 

分 类 号：TP392[自动化与计算机技术—计算机应用技术]