Advances on the use of meta-heuristic algorithms to optimize type-2 fuzzy logic systems for prediction, classification, clustering and pattern recognition

Searching the suitable values of parameters and structure of type-2 fuzzy logic systems is a complex task. Many types of meta-heuristic algorithms and more recently hybrid systems have been proposed in the literature to solve this complex problem. This is presently attracting tremendous attention from researchers in this area of study. In this paper, we review on the optimization of type-2 fuzzy logic systems based on meta-heuristic algorithms for time series prediction, classification, clustering and pattern recognition. It was found that the applications of hybrid genetic algorithms and particle swarm optimization are the main algorithms that help in realizing optimal design of type-2 fuzzy systems. Researchers can use this review as a starting point for further advancement as well as an exploration of other meta-heuristic algorithms that have received little or no attention from researchers.