Performance Enhancement of Adaptive Neuro-Fuzzy Inference System through Population-Based Algorithms

Abstract

The Adaptive Neuro-Fuzzy Inference System (ANFIS) is a powerful hybrid artificial intelligence model that combines the learning capabilities of neural networks with the reasoning of fuzzy logic. It is widely used to model complex relationships between input and output parameters across various domains. However, the performance of ANFIS is highly dependent on the optimal setting of its internal parameters, making their optimization a significant research focus. This work aims to enhance ANFIS performance by optimizing its parameters using seven well-recognized metaheuristic algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Salp Swarm Algorithm (SSA), and Grey Wolf Optimizer (GWO). The proposed hybrid models were evaluated on three different datasets. Experimental results demonstrate that the hybrid models, which integrate these optimization algorithms with ANFIS, achieve a significant performance improvement compared to the standard ANFIS model.