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This article shows you a sample analysis of the PSOt toolkit, concise and easy to understand, definitely will make your eyes shine, through the detailed introduction of this article I hope you can gain something.

Function optimization algorithm based on particle swarm toolbox to clear environment clearclc

Parameter initialization x_range=[-50,50]; % parameter x variation range y_range=[-50,50]; % parameter y variation range = [x_range;y_range]; % parameter variation range (composition matrix)Max_V = 0.2*(range(:,2)-range(:,1)); % maximum speed takes 10%~20%n=2; % dimension of the function to be optimized, in this example, there are only x and y arguments, so it is 2

PSOparams= [25 2000 24 2 0.9 0.4 1500 1e-25 250 NaN 0 0]; particle swarm optimization pso_Trelea_vectorized ('Rosenbrock ', n,Max_V,range,0,PSOparams) % invoke PSO core module function [out]=Rosenbrock(in)% optimization function x0=in(:,1:end-1);x1=in(:,2:end);

out = sum( (100*(x1-x0.^ 2).^ 2 + (x0-1).^ 2) , 2);

The above is an example analysis of the PSOt toolbox. Have you learned any knowledge or skills? If you want to learn more skills or enrich your knowledge reserves, please pay attention to the industry information channel.

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