Self-organizing fuzzy neural network using a fuzzy self-tuning multi-objective particle swarm optimization

This paper presents a novel fuzzy self-tuning multi-objective particle swarm optimization (FSTMOPSO) algorithm for self-organizing fuzzy neural networks (SOFNNs). The proposed approach addresses the multi-objective optimization problem by simultaneously optimizing three key factors: training accuracy, network complexity, and the smoothness of connection weights. FSTMOPSO employs a fuzzy self-tuning strategy that adaptively adjusts the parameters of each particle throughout the optimization process, thereby enhancing both convergence speed and solution quality. Additionally, a crowding operator based on Mahalanobis distance ensures diversity among the archived solutions, preventing clustering and enhancing coverage of the Pareto front. The FSTMOPSO-based SOFNN (FSTMOPSO-SORBF) can optimize the parameters and determine the number of fuzzy rules, achieving an optimal trade-off between network complexity and prediction accuracy. Furthermore, a detailed convergence analysis of FSTMOPSO-SORBF is provided to support its applicability in real-world scenarios. To verify the effectiveness of the proposed FSTMOPSO-SOFNN, four test cases are used: multi-objective optimization problems, the identification of the Mackey–Glass time series, effluent total phosphorus prediction, and short-term traffic flow forecasting. The simulation results demonstrate that the proposed FSTMOPSO-SOFNN not only achieves high approximation accuracy but also does so with minimal structural complexity.