基于改进PSO算法的移动机器人路径规划

摘要

Aiming at problems that convergence speed of particle swarm optimization(PSO)algorithm is fast but easy to fall into local extremum and the global search capcity of the bacterial foraging optimization (BFO ) algorithm is strong but the efficiency is low,a hybrid algorithm with global search capability and fast convergence by introducing the chemotaxis,migration and replication operations of BFO algorithms into particle swarm search process is proposed. On the basis of introduction of the principle and procedures of the basic BFO algorithm and PSO algorithm,the global path planning simulation experiment of the mobile robot is carried out by using the PSO algorithm,BFO algorithm and the hybrid algorithm respectively,at the same,number of iterations and the fitness curves of each algorithm is given respectively. The simulation results show that compared with the PSO algorithm and BFO algorithm,the proposed hybrid algorithm has the advantages of short search time and fewer iterations, which verifies the feasibility and effcetiveness of the hybrid algorithm in mobile robot path planning very well.%针对粒子群优化(PSO)算法收敛速度快但容易陷入局部极值和细菌觅食优化(BFO)算法全局搜索能力强但效率低的问题,提出了一种将BFO算法的趋化、迁徙和复制操作引入到粒子群搜索过程的具有全局搜索能力和快速收敛的混合算法.在BFO算法和PSO算法的原理、操作步骤基础上,分别使用了PSO算法、BFO法和混合算法对移动机器人进行全局路径规划仿真试验,并分别给出了各算法的迭代次数、适应值曲线.仿真结果表明:与PSO算法和BFO算法相比,所提出的混合算法具有搜索时间短、迭代次数少的优点,较好验证了混合算法在移动机器人路径规划方面的可行性和有效性.