Research On Improved Particle Swarm Optimization Algorithm

Particle Swarm Optimization(PSO)algorithm is an emerging biomimetic optimization algorithm based on the observation of the movement of birds and fish populations.Particle swarm optimization algorithm has the advantage of simple concept,less parameters,faster convergence,easier to achieve.Therefore,it has attracted a lot of attention from many scholars both at home and abroad.It has been applied to the application of function optimization,neural network training and fuzzy system control,and has achieved good results.But its shortcomings are easy to fall into the local minimum point,the search accuracy is not high,which also attracted many scholars on particle swarm optimization algorithm for a lot of improvements.In this paper,we first construct a new particle swarm optimization algorithm to update the formula,the update formula greatly increases the diversity of the algorithm.Then,the new updating formula is transformed into a discrete linear system.According to the theory of system stability,the parameter setting region is proposed to improve the stability of the particle swarm system.The stability of the algorithm is verified by numerical experiments.In addition,the hybrid particle swarm optimization algorithm is given by combining the harmony search algorithm with the particle swarm optimization algorithm,and the stability and feasibility of the algorithm are verified by numerical experiments.