Improvement Of Particle Swarm Optimization Algorithm And Its Application In Structural Optimization Of Cyclone Separator

Based on the heuristic stochastic evolution of swarm intelligence,Kenndey and Eberhart proposed a particle swarm optimization algorithm by simulating the group cooperation and competition behavior of birds and fishes in the process of predation.Compared to other swarm intelligence algorithm,particle swarm optimization algorithm has the characteristics of simple structure and easy realizationto solve the multi-objective optimization and dynamic optimization.After more than 20 years of development,PSO has formed a complete theoretical system.It has become an important research content of the evolutionary computation,and has been widely used in the theory of scientific and engineering applications.Particle swarm optimization algorithm has caused extensive attention.But the algorithmhas the disadvantages oflosingthe diversity and easily falling into local optimum in the later evolution,which led to the slow convergence and the decline of optimization accuracy.In order to ensure the accuracy of optimization and improve the convergence speed of the algorithm,this paper improved thepopulation topology structure and learning mechanism according to the previous work.The improved algorithm is applied to the parameter optimization of cyclone separator structure.The main contents of this paper are as follows:1?In order to increase the ability of information exchange among the population of particle swarm optimization algorithm(PSO),we proposed a full information mutation particle swarm optimization algorithm based on chaos topology(CFMPSO).The algorithm for periodic chaotic mixing of recombinant population topology structure in the process of evolution,and the particle information was fully utilized by the holographic strategy when the neighborhood of optimal individual was variation.This strategynot only enhanced the communication ability the convergence performance of particle swarm algorithm but also accelerated the evolution speed.The experimental results showed that the CFMPSO algorithm can achieve more accurate searching in solving most of the test functions with lesscalculation times,and fast optimization speed.2?Furtherstudying the algorithm characteristics and the population searching mechanism,itcame to the conclusion that the standard particle swarm algorithm is approximated a proportional integral(PI)control,due to its inherent integral attribute exists,which cause the slow convergence speed.In this paper,a fast particle swarm optimization algorithm based on differential strategy is proposed,which can improve the convergence speed of the standard particle swarm optimization and the improved algorithms.Comparing the simulation results of D-SPSO and D-FIPS,it shows that the improved algorithm of differential control strategy has less computation times and faster optimization speed.It is proved that the improved particle swarm optimization(PSO)algorithm based on this strategy is effective in solving the weakness of low efficiency and low speed.3?In order to verify the application ability of particle swarm optimization in engineering practice,the structure size of cyclone separator is taken as the optimization object.In order to meet the optimization goal of less pressure loss of cyclone separator and high separation efficiency four factors and three levels of experimental conditionsis used to design a test by Box-Behnken test.Theregression equation of the structure size is obtained,andthe improved particle swarm algorithm is used to optimize the regression equation.Through the comparison and analysis of the results of each algorithm,it shows that the improved algorithm can get better optimization results whilekeeping a faster speed of optimization.