Optimization of fuzzy controller based on multi-parent crossover dynamic evolutionary algorithm

Fuzzy control is a nonlinear control method; its performance depends on the fuzzy control rules, quantification factor and the scale factor. Because these multiple objectives are influence with each others, therefore it is very difficult to ensure the control effect only rely on expertise´s experience. In this paper, dynamic evolutionary algorithm is adopted to optimize the multi-objectives in fuzzy controller. In dynamic evolutionary algorithm, based on the competitive relationship between particles´ free energy and entropy in the phase space system, a new option strategy is put forward which is used to maintain species diversity; furthermore, multi-parent crossover operator is selected. This operator selects some individuals to form a space and then do searching in this space. This algorithm has strong ability to find the solutions of the problem, and it also run quickly compared with other traditional algorithms. Simulation results show that fuzzy controller optimized by the algorithm has good steady-state response time, steady-state error and overshoot.