Customer classification method and device based on improved particle swarm optimization algorithm

Abstract

The embodiment of the invention provides a customer classification method and device based on an improved particle swarm optimization algorithm, and the method comprises the steps: initializing a particle speed and a particle position according to a classification number and a feature dimension, and setting an initial value, so as to build an initial population of a particle swarm; performing iterative updating operation on the inertia weight, the particle speed and the particle position of the population according to a preset fitness function including the customer characteristic data until apreset iteration frequency is reached; after the number of iterations is preset, respectively carrying out selection operation, crossover operation and mutation operation on the particle swarm according to a genetic algorithm after each update for next iteration update until the iteration update reaches the total number of iterations or meets a convergence condition; and obtaining a clustering center according to the particle swarm reaching the total number of iterations or meeting the convergence condition, and classifying the customers. According to the method, through organic fusion of thegenetic algorithm, falling into a local optimal solution can be avoided, the later convergence speed is increased, and the search precision is improved.

Description

technical field [0001] The invention relates to the field of customer classification, in particular to a method and device for customer classification based on an improved particle swarm optimization algorithm. Background technique [0002] Particle Swarm Optimization (PSO) is a new evolutionary computing technology derived from the simulation of bird predation behavior proposed by Kennedy and Eberhart in 1995, through the swarm intelligence guidance generated by the cooperation and competition among the population particles Refine your search. This algorithm is a new global intelligent optimization algorithm, which has the advantages of simple implementation, few parameters to be set, and fast convergence speed. The main essential characteristics of the standard particle swarm optimization algorithm are fast convergence speed, simple operation and strong versatility. [0003] The particle swarm optimization algorithm combined with the cluster analysis method can realize t...

AI Technical Summary

Problems solved by technology

However, a large number of practices and studies have shown that the standard particle swarm optimization algorithm has defects such as poor local search ability, "premature convergence", and low iteration efficiency in the later stage. It is easy to fall into the disadvantage of local extremum, so the accuracy of the classification result is not high

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