Designing fuzzy rule base using hybrid elite genetic algorithm and tabu search: Application for control and modeling

摘要

In this paper, Hybrid Elite Genetic Algorithm and Tabu Search (HEGATS) is proposed to automatically generate Fuzzy rule base for fuzzy inference systems. The algorithm is used to simultaneously optimize the premise and consequent parameters of the fuzzy rules for the appropriate design of fuzzy system for Takagi-Sugeno zero-order. After finale selection of the new generation calculated by genetic algorithm, elitist solution is saved. In this step, tabu search is introduced to find the better neighboring of the elitist solution which will be introduced in the new generation. This hybridization of global and local optimization algorithms minimizes the number of iterations and computation time while maintaining accuracy and minimum response time. To demonstrate the effectiveness of the proposed algorithm, several numerical examples given in the literature for control and modeling systems are examined. Results prove the effectiveness of the proposed algorithm.