基于自适应差分进化算法的变循环发动机模型求解方法研究

In order to reduce the dependence of the traditional iterative algorithm on the initial value in solving the nonlinear model of variable cycle engine, the model solving problem was converted to the optimization problem of finding the minimum value. Differential evolution algorithm was introduced to solve the model, and an adaptive differential evolution algorithm was proposed. Using the evolution experience and roulette selection method, adaptive differential evolution algorithm can adaptively select the differential strategy and algorithm control parameters that are most suitable for the current population. For the model solving problem of the four typical operating points of the variable cycle engine, the effects of the control parameters of standard differential evolution algorithm on its performance were studied. And the optimal combinations of control parameters of standard differential evolution algorithm in solving the four typical operating points were obtained. The performance difference between adaptive differential evolution algorithm and standard differential evolution algorithm was compared. Finally, the effects of population size on the performance of adaptive differential evolution algorithm were studied. The results show that standard differential evolution algorithm has pretty robustness in solving engine model, and its optimal control parameters are not completely the same when solving different operating points. Compared with standard differential evolution algorithm using the best control parameters, adaptive differential evolution algorithm can increase the efficiency by more than 50% without affecting the robustness of the algorithm. Reducing the population size of adaptive differential evolution algorithm will improve the efficiency while destroying the robustness of the algorithm.