摘要
为寻求最优泊车路径,提高路径规划质量,提出一种基于粒子群优化(PSO)算法和人工鱼群算法(AFSA)相结合的混合式泊车路径规划模型。该模型利用改进后的惯性权重和学习因子对PSO算法进行优化,并将改进后的PSO算法与AFSA相结合,综合利用PSO算法的局部收敛性和AFSA的全局收敛性,提高混合算法的收敛速度和收敛精度,使得路径规划最优。最后,仿真实验结果表明,相较于单独使用AFSA,混合算法的收敛性更强,规划的泊车路径更优,且道路越复杂,混合算法的优势越明显。
In order to find the optimal parking path and improve the quality of path planning,a hybrid model for parking path planning based on particle swarm optimization(PSO)algorithm and artificial fish swarm algorithm(AFSA)is proposed in this paper.This model uses the improved inertia weight and learning factors to optimize PSO algorithm.The improved PSO algorithm is combined with AFSA to make comprehensive use of the local convergence of PSO algorithm and the global convergence of AFSA,so that the convergence speed and convergence accuracy of the hybrid algorithm are improved and the parking path planning is optimized.Finally,the simulation results show that compared with the separate AFSA,the hybrid algorithm has better convergence and planned parking paths.The more complex the road is,the more obvious the advantages of the hybrid algorithm become.
作者
谢劲
胡光元
闫明
李丹阳
XIE Jin;HU Guang-yuan;YAN Ming;LI Dan-yang(School of Computer Science,Shenyang Aerospace University,Shenyang 110136,China)
出处
《控制工程》
CSCD
北大核心
2022年第12期2357-2364,共8页
Control Engineering of China
基金
省级大学生创新创业训练计划项目(S202010143011)。
