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Improving Adaptive Neuro-Fuzzy Inference System Based on a Modified Salp Swarm Algorithm Using Genetic Algorithm to Forecast Crude Oil Price

  • Mohamed Abd ElazizEmail author
  • Ahmed A. Ewees
  • Zakaria Alameer
Original Paper

Abstract

This paper presents a novel forecasting model for crude oil price which has the largest effect on economies and countries. The proposed method depends on improving the performance of the adaptive neuro-fuzzy inference system (ANFIS) using a modified salp swarm algorithm (SSA). The SSA simulates the behaviors of salp swarm in nature during searching for food, and it has been developed as a global optimization method. However, SSA still has some limitations such as getting trapped at a local point. Therefore, this paper uses the genetic algorithm to improve the behavior of SSA. The proposed model (GA-SSA-ANFIS) aims to determine the suitable parameters for the ANFIS by using the GA-SSA algorithm since these parameters are considered as the main factor influencing the ANFIS’s prediction process. The results of the GA-SSA-ANFIS are compared to other models, including the traditional ANFIS model, ANFIS based on GA (GA-ANFIS), ANFIS based on SSA (SSA-ANFIS) ANFIS based on particle swarm optimization (PSO-ANFIS), and ANFIS based on grey wolf optimization (GWO-ANFIS). The results show the superiority and high performances of the GA-SSA-ANFIS over the other models in predicting crude oil prices.

Keywords

Salp swarm optimization Adaptive neuro-fuzzy inference system Crude oil price Forecasting Genetic algorithm 

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Copyright information

© International Association for Mathematical Geosciences 2019

Authors and Affiliations

  • Mohamed Abd Elaziz
    • 1
    Email author
  • Ahmed A. Ewees
    • 2
    • 3
  • Zakaria Alameer
    • 4
    • 5
  1. 1.Department of Mathematics, Faculty of ScienceZagazig UniversityZagazigEgypt
  2. 2.University of BishaBishaKingdom of Saudi Arabia
  3. 3.Department of ComputerDamietta UniversityDamiettaEgypt
  4. 4.School of Resources and Environmental EngineeringWuhan University of TechnologyHubeiPeople’s Republic of China
  5. 5.Mining and Petroleum Department, Faculty of EngineeringAl-Azhar UniversityQenaEgypt

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