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Application of Improved Artificial Intelligence with Runner-Root Meta-Heuristic Algorithm for Dairy Products Industry: A Case Study
International Journal on Artificial Intelligence Tools ( IF 1.1 ) Pub Date : 2020-05-20 , DOI: 10.1142/s0218213020500086
Alireza Goli 1 , Ehsan Moeini 2 , Ahmad M. Shafiee 3 , Mohammad Zamani 4 , Elham Touti 5
Affiliation  

As the dairy products have a short consumption period, the accurate prediction of their demand is very important for the dairy industry. Accordingly, this research specifically addresses the prediction of dairy product demand (DPD). The main contribution of this research is to provide an integrated framework based on statistical tests, time-series prediction and artificial intelligence with the runner-root algorithm (RRA) as a novel meta-heuristic algorithm to obtain the best prediction of DPD in Iran. First, a series of economic and social indicators that seemed to be effective in the demand for dairy products are identified and the ineffective indices are eliminated. Next, the artificial intelligence tools including MLP, ANFIS, and LSTM are implemented and improved with the help of RRA. The designed hybrid methods are implemented by using data from 2013 to 2017 of the Iran diary industry. This novel algorithm is compared to gray wolf optimization, invasive weed optimization, and particle swarm optimization. The results show that the proposed MLP-RRA has the most ability to improve by using meta-heuristic algorithms. The coefficient of determination is 98.19%. Moreover, in each artificial intelligence tools, RRA causes better results than the other tested algorithms. The highly accurate results confirm that the proposed hybrid methods based on the RRA algorithm are able to improve the prediction of demand for various products.

中文翻译:

改进的人工智能与 Runner-Root 元启发式算法在乳制品行业的应用:案例研究

由于乳制品消费周期短,准确预测其需求量对乳制品行业非常重要。因此,本研究专门针对乳制品需求 (DPD) 的预测。本研究的主要贡献是提供了一个基于统计测试、时间序列预测和人工智能的集成框架,并将 runner-root 算法 (RRA) 作为一种新颖的元启发式算法,以获得伊朗 DPD 的最佳预测。首先,识别出一系列对乳制品需求似乎有效的经济社会指标,剔除无效指标。接下来,在 RRA 的帮助下实现和改进了包括 MLP、ANFIS 和 LSTM 在内的人工智能工具。设计的混合方法是利用伊朗乳业 2013 年至 2017 年的数据实现的。这种新颖的算法与灰狼优化、侵入性杂草优化和粒子群优化进行了比较。结果表明,所提出的 MLP-RRA 通过使用元启发式算法具有最大的改进能力。决定系数为98.19%。此外,在每个人工智能工具中,RRA 比其他测试算法产生更好的结果。高度准确的结果证实,所提出的基于 RRA 算法的混合方法能够改进对各种产品的需求预测。结果表明,所提出的 MLP-RRA 通过使用元启发式算法具有最大的改进能力。决定系数为98.19%。此外,在每个人工智能工具中,RRA 比其他测试算法产生更好的结果。高度准确的结果证实,所提出的基于 RRA 算法的混合方法能够改进对各种产品的需求预测。结果表明,所提出的 MLP-RRA 通过使用元启发式算法具有最大的改进能力。决定系数为98.19%。此外,在每个人工智能工具中,RRA 比其他测试算法产生更好的结果。高度准确的结果证实,所提出的基于 RRA 算法的混合方法能够改进对各种产品的需求预测。
更新日期:2020-05-20
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