The application of complex dual population genetic algorithms to optimizing the fuzzy control

Refers to the thought of species competition mechanism to present dual population genetic algorithms based on complex-valued encoding, we use real genes string and imaginary genes string to express an individual, two populations paralleled evolve, we use grouping crossover modeled “Tianji horse” among species; and use immigration method based on the rate of golden mean between populations, to increase diversity patterns, speed up the convergence, and improve the searching capability of the algorithm. Combining the merits of genetic algorithms and fuzzy control, membership functions of fuzzy control, scaling factor and quantitative factors optimization are optimized by the improved genetic algorithm, the simulation result shows that the proposed method has stronger evolutionary capacity compared with the traditional dual population genetic algorithms, the effect of the control for fuzzy controller optimized is good.