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Today, I will talk to you about how to use the particle swarm toolbox PSOt, which may not be well understood by many people. In order to make you understand better, the editor has summarized the following contents for you. I hope you can get something according to this article.

The PSOt particle swarm toolbox PSOt, developed by Brian Birge, a professor in the Department of Aeronautics, Astronautics and Mechanics of North Carolina State University, can be used to solve optimization problems easily.

The main function of the toolbox: pso_Trelea_vectorized (functname,D,mv,VarRange,minmax,PSOparams,plotfcn,PSOseedValue) functname: optimization function name. D: the dimension with optimization function. Mv: the range of maximum speed. VarRange: range of values for particle position. Minmax:1 indicates that the algorithm looks for the maximum target. 0 indicates that the algorithm is looking for a minimum target. 2 means that the algorithm looks for the nearest value target. Plotfcn: draw an image function. PSOseedValue: initializes the particle position.

PSOparams: the specific parameters used by the algorithm. For example, PSOparams= [25 2000 24 2 0.9 0.4 1500 1e-25 250 NaN 00]; the first parameter represents the number of intervals between the calculation processes displayed in the MATLAB command window, and 25 indicates that the algorithm displays the results every 25 iterations. If the value is zero, the intermediate process of calculation is not displayed. The second parameter represents the maximum number of iterations of the algorithm. After the maximum number of iterations is satisfied, the algorithm stops. Here, the maximum number of reception is 2000. The third parameter represents the number of individuals in the population, the more individuals, the easier the convergence, but the slower the convergence speed of the algorithm. The fourth and fifth parameter is the acceleration parameter of the algorithm, which affects the local optimal value and the global optimal value respectively. The sixth and seventh parameter represents the weights at the beginning and end of the algorithm, and the weights at other times are obtained by linear calculation. The eighth parameter indicates that when the number of iterations exceeds this value, the weight is the smaller of the sixth and seventh parameters. The ninth parameter represents the termination threshold of the algorithm, and the algorithm terminates when the change of the optimal value of the corresponding population is less than this threshold in two consecutive iterations. The tenth parameter represents the threshold used to terminate the algorithm. The eleventh parameter indicates whether the optimization problem has constraints, and NaN indicates that there are no constraints. The twelfth parameter indicates the type of particle swarm optimization algorithm used. The thirteenth parameter indicates whether the population initialization uses the specified random seed, 0 means random generation, and 1 indicates that the user generates it. After reading the above, do you have any further understanding of how to use the particle swarm toolbox PSOt? If you want to know more knowledge or related content, please follow the industry information channel, thank you for your support.

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