Abstract/Summary

Introduction of fuzzy set enabled the modeling of uncertain and noisy information. Type-2 fuzzy set took this further ahead by allowing type-2 fuzzy membership function to be fuzzy itself. In this work, we describe an interval type-2 fuzzy logic system (FLS). The training of interval type-2 FLS was provided in a supervised manner by using metaheuristic algorithms. We comprehensively illustrated formulation of interval type-2 FLS into an optimization problem. A precise genotype (a real vector) mapping of FLS was described. This work finds the extent of the learning capability of FLS. Since the FLS learning is computationally difficult and costly, which we described in detail in this work, a comprehensive comparison between the performances of the metaheuristic algorithms was offered. The obtained results suggest that FLS learning was faster at the initial iterations of the metaheuristic learning, but tend to slow and get stuck in local minima. However, the metaheuristic algorithms, differential evaluation and bacteria foraging optimization offered significantly better results when compared to artificial bee colony, gray wolf optimization, and particle swarm optimization.