Path Planning Based on Multi-Strategy Fusion Particle Swarm Optimization Algorithm
In order to solve the problem that the traditional particle swarm optimization algorithm is easy to fall into the local optimal in the path planning,which makes the planned path long and easy to stall due to the decrease of population diversity in the late search period,a multi-strategy fusion particle swarm optimi-zation algorithm is proposed and applied to the path planning.Firstly,the particle position updating method of the midvertical algorithm is used to improve the convergence rate of particles.Secondly,the strategy of generating explosive particles near the optimal particles is used to make the algorithm jump out of the local optimal.Then the linear dynamic inertia weight adjustment method is introduced to increase the searching a-bility of the algorithm.Finally,the local search strategy of the global optimal solution is adopted in the path planning application.The optimal path obtained at the later stage of the algorithm is then locally searched to obtain a better path,which increases the path planning ability of the robot.The simulation results show that the multi-strategy fusion particle swarm optimization algorithm has higher path search capability in path planning.
path planningmidvertical algorithmexplosive particleglobal optimal solution local search
NETL
NSTL
万方数据
维普
