基于改进狮群算法和BP神经网络模型的房价预测

doi:10.6040/j.issn.1672-3961.0.2020.482

Abstract

Abstract: The lion swarm optimization algorithm combined the migration mechanism and spiral search mechanism of seagull algorithm to enhance the local search ability; the global search performance of lion swarm optimization algorithm was enhanced by adding supervision mechanism. The particle swarm optimization algorithm and the lion swarm optimization algorithm were used as the comparison algorithm, and the advantages of the improved algorithm were verified on the common test functions. The improved lion swarm optimization algorithm was used to optimize the BP neural network model to study the problem of housing price prediction, and the price of second-hand housing in Qingdao could be effectively predicted through relevant indicators such as house type and area. The improved lion swarm optimization algorithm was used to optimize the weights and biases of the BP neural network to improve the convergence speed and training accuracy of the BP neural network. The test results showed that the SLSO-BP model proposed in the study had a better prediction effect on the problem of housing price prediction.

Key words: lion swarm algorithm, spiral search, supervision mechanism, BP neural network, housing price prediction

CLC Number:

TP183

DING Fei, JIANG Mingyan. Housing price prediction based on improved lion swarm algorithm and BP neural network model[J].Journal of Shandong University(Engineering Science), 2021, 51(4): 8-16.

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