混合粒子群算法在变电站巡检机器人缺陷定点跟踪上的应用

Application of Hybrid Particle Swarm Algorithm in Defect Fixed Point Tracking Function of Substation Inspection Robot

为了提高变电站巡检机器人的巡视效率,采用混合粒子群算法对缺陷定点跟踪时的路线进行规划设计。首先对传统粒子群算法进行改进,引入遗传算法中的交叉和变异操作,粒子通过同个体极值、群体极值的交叉以及自身变异的方式来搜索最优解;其次输入待巡视节点之间的距离矩阵、粒子数量、迭代次数,采用MATLAB语言编写的混合粒子群算法可以快速计算出各节点的巡视次序及巡检总路程。

In order to improve the inspection efficiency of the substation inspection robot,a hybrid particle swarm optimization algorithm is used to design the route of the defect tracking.Firstly,the traditional particle swarm optimization algorithm is improved,and the crossover and mutation operations in genetic algorithm are introduced.Particles search for the optimal solution through crossover with individual extreme values,group extremes,and self-mutation.Secondly,by inputting the distance matrix,the number of particles and the number of iterations between the nodes to be inspected,the hybrid particle swarm optimization algorithm written in MATLAB language can quickly calculate the inspection order and the total inspection distance of each node.