摘要
针对智能飞行器航迹优化问题,由于在空中飞行受到累计水平和垂直校正约束及飞机转弯性能约束条件下,建立了多约束随机进化因子修正误差的蚁群算法。通过动态仿真结果表明,本算法能够快速生成优化的可行航迹,结合遗传算法改进的蚁群算法,并创新性地加入剪枝策略,减少网络节点数量,加快算法的收敛速度,并能有效降低算法的复杂度。
To deals with the flight path optimization problem of intelligent aircraft,due to the constraints of cumulative horizontal and vertical correction and aircraft turning performance in the air,an ant colony algorithm with multi-constraint random evolutionary factors was established to correct errors.The results of dynamic simulation show that this algorithm can generate optimized feasible track quickly,the ant colony algorithm was improved with genetic algorithm and pruning strategy was innovatively added to reduce the number of network nodes,the convergence speed of the algorithm is accelerated and the complexity of the algorithm is reduced effectively.
作者
詹棠森
熊峰
薛广富
赵世栋
施明勇
汤可宗
ZHAN Tangsen;XIONG Feng;XUE Guangfu;ZHAO Shidong;SHI Mingyong;TANG Kezong(School of Information Engineering,Jingdezhen Ceramic University,333403,Jingdezhen,Jiangxi,PRC)
出处
《江西科学》
2022年第4期625-632,653,共9页
Jiangxi Science
基金
国家自然科学基金项目(71763013,61702239)
江西省教育厅重点科研项目(GJJ190907)。
关键词
航迹规划
随机进化因子
蚁群算法
剪枝
path planning
random evolutionary factor
ant colony algorithm
pruning
