The invention discloses a wing design optimization method based on agent-assisted multi-initial-point
simulated annealing, and belongs to the technical field of optimization design. Comprising the steps of 1, initializing
algorithm parameters and a training
data set; 2, constructing an agent model; 3, executing multi-initial-point parallel
simulated annealing, and generating a batch of candidate new solution sets; 4, executing a double-elite active learning strategy based on the new solution set, screening the most potential sample to carry out real evaluation, and updating the agent model; 5, cooling the temperature and reducing the step length; 6, judging whether the cumulative evaluation times of the expensive objective function reach the set maximum evaluation times or not; if not, returning to the step 2; if yes, optimization is stopped; and finally, traversing the training
data set, and selecting a sample point with the minimum real objective function value as a
global optimal solution. According to the method, a multi-initial-point parallel
simulated annealing search mechanism and a double-elite active learning strategy are combined, and the
global optimal solution is quickly approached under the limited
simulation times.