Description

The particle swarm optimization (PSO) algorithm is widely everyday in the field. To overcome these drawbacks, established a new T–S fuzzy system restrictions probing strategy called OptiFel with a mixt multi swarm PSO (MsPSO) to enhance the probing performance. The multiple subswarms approach proposed in this paper is greatly helpful for finding the optimal restrictions suitable for the subspaces of the T–S fuzzy model. With the amended MsPSO, OptiFel can create a good fuzzy system model with high accuracy and strong generalization capacity. Data-driven design of exact and reliable Takagi Sugeno (T–S) fuzzy systems has fascinated a lot of attention, where the model edifices and parameters are central and often solved in an optimization structure. Efforts were put towards topology of statement, restriction adjustment, initial diffusion of particles and efficient issue unraveling competencies. The main strength of PSO is its fast conjunction, which compares constructively with many global optimization algorithms like Genetic Algorithms (GA), Simulated Annealing (SA) and other global optimization algorithms. Takagi-Sugeno (TS) fuzzy models, with local models often chosen linear or affine.