Optimization of Fuzzy Rule Based on Adaptive Genetic Algorithm and Ant Colony Algorithm

Genetic algorithm has been widely used in the various optimal problems. Its application in the fuzzy control is still limited by factors such as local optimal and premature convergence. Therefore, this paper proposes that fuzzy control rule were adjusted together by using hybrid algorithm based on genetic algorithm and ant colony algorithm. Genetic algorithm generates initial rule candidate and develop initial pheromone of ant colony algorithm. Updating of pheromone, ant colony operation replaces selecting operation of genetic algorithm and obtains better candidate. Then, carry out subsequence operation of genetic algorithm. The simulation results show that the hybrid algorithm make fuzzy controller get better controlling efficiency than general genetic algorithm optimization.