To improve the accuracy and responsiveness of implicit generalized prediction control algorithm(IGPC),this paper proposes an implicit generalized prediction algorithm that improves the optimization of the aquila optimizer(AO) algorithm.Firstly,inertial weights are added to the four search stages of the Aquila algorithm to balance the search capabilities of each stage.At the same time,it avoids the problem of local optimality.Then add the Aquila algorithm to the rolling optimization part of the implicit generalized prediction.Gradient optimization is used to find the optimal control input under unconstrained conditions.Then,when there are constraints,the implicit generalized prediction improved by Aquila is used for optimization.Finally,the method is applied to the circulating fluidized bed boiler(CFBB) for verification.From the perspective of time,the average simulation speed of implicit generalized prediction optimized by the improved AO algorithm is 3.9831 s,which is significantly faster than the average time of implicit generalized prediction of 5.9531 s,and it can have a good tracking effect within a small error.The simulation results show the feasibility of the algorithm and its superior control performance.