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BP Network Model Based on SCLBOA for House Price Forecasting.

Xudong Ji¹, Xiao-Fang Ji¹, Hongxing Wei¹

¹School of mechanical engineering and automation, Beihang University, Beijing 100191, China.

Computational Intelligence and Neuroscience

|October 24, 2022

Summary

This study introduces an improved Butterfly Optimization Algorithm (BOA) called SCLBOA, enhancing convergence speed and accuracy for complex optimization tasks. The enhanced algorithm demonstrates superior performance in mathematical optimization and practical applications like housing price prediction.

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Area of Science:

Computational Intelligence
Swarm Intelligence Algorithms
Metaheuristic Optimization

Background:

The Butterfly Optimization Algorithm (BOA) is a nature-inspired metaheuristic algorithm with potential but requires enhancements.
Existing BOA implementations face limitations in convergence speed and solution accuracy for complex problems.

Purpose of the Study:

To improve the Butterfly Optimization Algorithm (BOA) by enhancing its convergence speed and accuracy.
To introduce a novel variant, SCLBOA, incorporating a logical chaotic map and Lévy flight mechanism.
To validate the effectiveness of SCLBOA on standard test functions and a real-world prediction task.

Main Methods:

Developed SCLBOA by integrating a logical chaotic map for population initialization and a Lévy flight mechanism into the BOA framework.
Evaluated SCLBOA performance against other optimization methods using a suite of standard mathematical test functions.
Applied SCLBOA to optimize a Backpropagation (BP) neural network for a Boston housing price prediction task (SCLBOA-BP).

Main Results:

SCLBOA demonstrated significant improvements in convergence speed and optimization accuracy compared to existing methods.
Experimental results confirmed SCLBOA's capability for high-precision, fast, and effective global optimization on test functions.
The SCLBOA-BP model successfully predicted housing prices, validating the algorithm's practical applicability.

Conclusions:

The proposed SCLBOA algorithm offers a superior approach to mathematical optimization problems.
SCLBOA effectively addresses the limitations of the standard BOA, providing faster convergence and higher accuracy.
The successful application in housing price prediction highlights SCLBOA's potential for real-world machine learning tasks.