Open Access

ARTICLE

Optimization of Interval Type-2 Fuzzy Logic System Using Grasshopper Optimization Algorithm

Saima Hassan¹, Mojtaba Ahmadieh Khanesar², Nazar Kalaf Hussein³, Samir Brahim Belhaouari^4,*, Usman Amjad⁵, Wali Khan Mashwani⁶

1 Institute of Computing, Kohat University of Science and Technology, Kohat, Pakistan
2 Department of Mechanical, Materials and Manufacturing Engineering, Faculty of Engineering, University of Nottingham, UK
3 College of Computer Sciences and Mathematics, Tikrit University, Iraq
4 Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar
5 Department of Computer Science, Sir Syed University of Engineering and Technology, Pakistan
6 Institute of Numerical Sciences, Kohat University of Science and Technology, Kohat, Pakistan

* Corresponding Author: Samir Brahim Belhaouari. Email:

(This article belongs to this Special Issue: Recent Advances in Metaheuristic Techniques and Their Real-World Applications)

Computers, Materials & Continua 2022, 71(2), 3513-3531. https://doi.org/10.32604/cmc.2022.022018

Received 24 July 2021; Accepted 06 October 2021; Issue published 07 December 2021

Abstract

The estimation of the fuzzy membership function parameters for interval type 2 fuzzy logic system (IT2-FLS) is a challenging task in the presence of uncertainty and imprecision. Grasshopper optimization algorithm (GOA) is a fresh population based meta-heuristic algorithm that mimics the swarming behavior of grasshoppers in nature, which has good convergence ability towards optima. The main objective of this paper is to apply GOA to estimate the optimal parameters of the Gaussian membership function in an IT2-FLS. The antecedent part parameters (Gaussian membership function parameters) are encoded as a population of artificial swarm of grasshoppers and optimized using its algorithm. Tuning of the consequent part parameters are accomplished using extreme learning machine. The optimized IT2-FLS (GOAIT2FELM) obtained the optimal premise parameters based on tuned consequent part parameters and is then applied on the Australian national electricity market data for the forecasting of electricity loads and prices. The forecasting performance of the proposed model is compared with other population-based optimized IT2-FLS including genetic algorithm and artificial bee colony optimization algorithm. Analysis of the performance, on the same data-sets, reveals that the proposed GOAIT2FELM could be a better approach for improving the accuracy of the IT2-FLS as compared to other variants of the optimized IT2-FLS.

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Keywords

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