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Fuzzy Takagi Sugeno System Optimization using Hybrid Particle Swarm Optimization and Tabu Search Learning Algorithm

N. Talbi, K. Belarbi


A Hybrid Particle Swarm Optimization and Tabu Search (HPSOTS) algorithm is proposed for generating fuzzy systems. The algorithm dynamically generates the fuzzy rule base according to different environments. At each step of PSO, we calculate the current solution and seek the best nearby solution by Tabu search. The tabu list records the final movements visited by the TS algorithm and speeds up the convergence to global optimum. This operation minimizes the number of generations and computation time while maintaining accuracy. The algorithm was tested to generate fuzzy rule base for two nonlinear systems. A comparison with other methods in the literature demonstrates the effectiveness of the proposed algorithm.


Fuzzy Takagi Sugeno system, optimization, Tabu Search, Particle swarm optimization.

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