Fuzzy Control Based On SA And GA

The potential genetic algorithm are systematically analyzed. Improved accelerating genetic algorithm and hybrid accelerating genetic algorithm are proposed for lots of Multi dimension, multiapex, nonlinear, discontinuity nonprotruding about complex parameter optimization problem in the model of the object controlled. Comparisons with some usual optimization methods were made through the application of the model in practical. Results showed that the above improved accelerating genetic algorithm have the features of convenient, fast, constringency and steady. They could be applied extensively in the optimal problems.At the same time crossover operator and mutation operator are modified for binary accelerating genetic algorithm in this paper. Improved accelerating genetic algorithm is presented. The total optimization ability is improved. According to population's fact, crossover operator and mutation operator arc adjusted foraccelerating genetic algorithm in evolution in time. Adaptive accelerating genetic algorithm for dynamic crossover and mutation operator are presented. Early constringency is conquered. The total optimization ability is improved. Binary coding need frequent coding and decoding, and the amount of calculation is big. Although real coding genetic algorithm needn't frequent code and decode, local searching ability of real coding genetic algorithm is sometime difference. Simplex's algorithm, simulated annealing algorithm or Hooke-Jeeves algorithm is added in real coding genetic algorithm, Simplex hybrid accelerating genetic algorithm, simulated annealing hybrid accelerating genetic algorithm and Hooke-Jeeves hybrid accelerating genetic algorithm are presented; at a certain extent the calculation steps of algorithm is reduced, searching efficiency, global optimization ability and solution's precision are improved. Simulated annealing algorithm is improved, an improved simulated annealing algorithm is established and searching efficiency is improved for simulated annealing algorithm. Simulated annealing hybrid accelerating genetic algorithm is applied to optimization the membership function of fuzzy control system, in the practice .The results of simulation manifests the fuzzy control optimized has quick response speed andgood robust.