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

TitleAn Improved Artificial Intelligence Based on Gray Wolf Optimization and Cultural Algorithm to Predict Demand for Dairy Products: A Case Study
Publication TypeJournal Article
Year of PublicationIn Press
AuthorsGoli, A., H. K. Zare, R. Tavakkoli-Moghaddam, and A. Sadeghieh
JournalInternational Journal of Interactive Multimedia and Artificial Intelligence
ISSN1989-1660
IssueIn Press
VolumeIn Press
NumberIn Press
Date Published03/2019
Pagination1-8
Abstract

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.

KeywordsArtificial Intelligence, Cultural Algorithm, Gray Wolf Optimization, Neural Network, Regression, Time Series
DOI10.9781/ijimai.2019.03.003
URLhttp://www.ijimai.org/journal/sites/default/files/files/2019/03/ip2019_03_03_pdf_50278.pdf
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ip2019_03_03.pdf873.59 KB