基于分布式混合灰狼蝗虫优化算法航班延误预测

针对航班延误成因复杂、数据量大,传统模型预测准确率差、效率低的问题,提出一种基于灰狼优化算法和蝗虫优化算法的混合算法建立航班延误预测模型。将灰狼优化算法的等级机制引入到蝗虫优化算法中,在种群迭代时生成不同层级狼群共同指导种群的进化方法,避免单个体对种群进化的绝对控制。采用Spark大数据框架设计分布式混合灰狼蝗虫优化算法提高模型运行效率。仿真实验结果表明:分布式混合灰狼蝗虫算法能够提高航班延误预测准确率和运行效率。

物联环境下拥堵道路汽车最优通行路径选择

研究并提出了一种基于物联环境下拥堵道路汽车最优通行路径选择的“分布式”算法,通过计算物联环境下汽车行驶路况信息素,构建算法模型,推演拥堵道路汽车最优通行路径信息素发展趋势,标定拥堵道路重复路径优选信息,栅格化处理容易将通行道路信息素和路面障碍物重叠,由此选择基于局部最优和整体最优的拥堵道路汽车通行可行性路径。经过最优道路扩展运算和通行路径细化优选分析,在模拟运行环境下,测试验证了该算法优选方案及其决策支持下的最优通行路径的合理性、安全性与经济性。

PARAMETER IDENTIFICATION OF LUGRE FRICTION MODEL FOR FLIGHT SIMULATION SERVO SYSTEM BASED ON ANT COLONY ALGORITHM

In light of the high nonlinearity of LuGre friction model, a novel method based on ant colony algorithm(ACA) for identifying the friction parameters of flight simulation servo system is proposed. ACA is a parallelized bionic optimization algorithm inspired from the behavior of real ants, and a kind of positive feedback mechanism is adopted in ACA. On the basis of brief introduction of LuGre friction model, a method for identifying the static LuGre friction parameters and the dynamic LuGre friction parameters using ACA is derived. Finally, this new friction parameter identification scheme is applied to a electric-driven flight simulation servo system with high precision. Simulation and application results verify the feasibility and the effectiveness of the scheme. It provides a new way to identify the friction parameters of LuGre model.