Improved PSO and Its Application in Robot Path Planning
When solving the multi-path robot path planning problem,standard particle swarm optimization algo-rithm is prone to precocious convergence into the local optimal path.In addition,the final search accuracy is often not high because only the information of the optimal group and the optimal individual is used.This paper presents an im-proved particle swarm optimization algorithm.Firstly,in order to improve the optimization accuracy of the algorithm,we adjusted the inertia weight coefficient and acceleration coefficient of the algorithm by using the monotonicity of trigonometric function.The key parameters can be matched optimally in the whole running of the algorithm.Secondly,in order to effectively avoid the algorithm falling into local optimum,the mutation operation was performed on the pop-ulation corresponding to the poor fitness function value,and the new particles were randomly generated to replace this part of the poor particles.Finally,through the comparison experiments of simple and complex path planning,it was verified that the improved algorithm has the advantages of high accuracy and good robustness when solving the robot path planning problem.
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