Design of fuzzy logic system framework using evolutionary techniques

Designing fuzzy logic system is one of the most popular and research-demanding NP-hard problems. It involves numerous parameters like shape and location of fuzzy sets, antecedents and consequents of fuzzy rule base and other strategic parameters like aggregation, implication and defuzzification methods. Time series forecasting has also become increasingly popular for the applications like share market prediction, weather forecasting. Many researchers have investigated the use of fuzzy logic system for forecasting of time series. In this paper, the authors have investigated the design framework of fuzzy logic systems for forecasting benchmark Mackey–Glass time series. Designing fuzzy logic systems is a class of NP-hard problems which is evolved using most popular and recent evolutionary algorithms. Authors have evolved fuzzy logic system using genetic algorithm, particle swarm optimization, artificial bee colony optimization, firefly algorithm and whale optimization algorithm. Finally, from simulations, it is found that whale optimization algorithm requires less time and shows fuzzy system predictions are more precise than others.

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