Particle swarm algorithm based on artificial intelligence semi-supervised clustering target

Abstract

The invention provides a particle swarm algorithm based on an artificial intelligence semi-supervised clustering target, and the algorithm comprises the steps: S1, inputting a data set, and randomly selecting K elements as a clustering center; S2, updating the clustering center, calculating the self-adaptive quantity of the current K value, comparing the self-adaptive quantity with the previous self-adaptive quantity, and retaining the K value with higher self-adaptive quantity; S3, repeatedly executing the step S1S2 until the optimal K clustering centers are obtained; S4, encoding and initializing the particles according to the optimal K clustering centers to obtain individual optimal and global optimal solutions; S5, performing dynamic clustering on the particles, obtaining new positionsof the particles and judging whether updating is needed or not; S6, performing immune disturbance and chaotic disturbance processing on the particles; S7, calculating an individual optimal solution and a global optimal solution of the current particle, comparing with the last time, and judging whether to update the individual optimal solution and the global optimal solution or not; S8, repeatingthe step S5 and the step S7, and if the current number of iterations reaches a preset value, exiting the algorithm.

Description

technical field [0001] The invention relates to the technical field of data statistics, in particular to a particle swarm algorithm based on artificial intelligence semi-supervised clustering objectives. Background technique [0002] The particle swarm optimization algorithm is a swarm intelligence optimization algorithm formed by simulating the mutual cooperation among individuals in the bird flock. As a typical representative of the swarm intelligence algorithm, the particle swarm optimization algorithm has been proved to be an effective global optimization algorithm. The algorithm has the advantages of simple principle, easy implementation, and few parameters, and has attracted the attention of many researchers once it was proposed. Mother, this algorithm has been widely used in engineering optimization, image processing, data control and other fields. [0003] The advantages of the traditional particle swarm optimization algorithm are obvious, but with the increase of t...

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Problems solved by technology

[0003] The advantages of the traditional particle swarm optimization algorithm are obvious, but with the increase of the complexity of the environment, when the traditional algorithm is used, the sensitivity of the clustering center increases, there are too many empty clusters, and the class labels have insufficient influence on the clustering results. serious

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