An Improved Artificial Intelligence Based on Gray Wolf Optimization and Cultural Algorithm to Predict Demand for Dairy Products: A Case Study

Alireza Goli ^[1] ; Hassan Khademi Zare ^[1] ; Reza Tavakkoli-Moghaddam ^[2] ; Ahmad Sadeghieh ^[1]
1. [1] Yazd University
  
  Yazd University
  
  Irán
2. [2] University of Tehran
  
  University of Tehran
  
  Irán
Localización: IJIMAI, ISSN-e 1989-1660, Vol. 5, Nº. 6, 2019 (Ejemplar dedicado a: Special Issue on Uses Cases of Artificial Intelligence, Digital Marketing and Neuroscience), págs. 15-22
Idioma: inglés
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Resumen
- This paper provides an integrated framework based on statistical tests, time series neural network and improved multi-layer perceptron neural network (MLP) with novel meta-heuristic algorithms in order to obtain best prediction of dairy product demand (DPD) in Iran. At first, a series of economic and social indicators that seemed to be effective in the demand for dairy products is identified. Then, the ineffective indices are eliminated by using Pearson correlation coefficient, and statistically significant variables are determined. Then, MLP is improved with the help of novel meta-heuristic algorithms such as gray wolf optimization and cultural algorithm.
  
  The designed hybrid method is used to predict the DPD in Iran by using data from 2013 to 2017. The results show that the MLP offers 71.9% of the coefficient of determination, which is better compared to the other two methods if no improvement is achieved.