BP Network Model Based on SCLBOA for House Price Forecasting

Abstract

Butterfly optimization algorithm (BOA) is a new swarm intelligence algorithm mimicking the behaviors of butterflies. However, there is still much room for improvement. In order to enhance the convergence speed and accuracy of the BOA, we present an improved algorithm SCLBOA based on SIBOA, which incorporates a logical mapping and a Lévy flight mechanism. The logical chaotic map is used for population initialization, and then the Lévy flight mechanism is integrated into the SCLBOA algorithm. To evaluate the performance of the SCLBOA, we conducted many experiments on standard test functions. The simulation results suggest that the SCLBOA is capable of high-precision optimization, fast convergence, and effective global optimization, all of which show that our method outperforms other methods in solving mathematical optimization problems. Finally, the BP network is optimized according to the SCLBOA (SCLBOA-BP) to further verify the availability of the algorithm. Simulation experiments prove the practicability of this method by building a Boston housing price prediction model for training.