Adaptive GASA algorithm for multidisciplinary design optimization

In the application of multidisciplinary design optimization, intelligent optimization algorithm can better avoid the problems of “falling into local optimum” or “unexpected termination” caused by conventional numerical optimization methods. In this paper, an adaptive GASA algorithm is proposed, which combines the global parallel search ability of genetic algorithm with the probability jump characteristic of simulated annealing algorithm, and uses adaptive mechanism to improve the population operation of intelligent optimization algorithm. Finally, the proposed method is verified by using a calculation example in combination with the collaborative optimization strategy. The results show that the proposed method can effectively improve the optimization efficiency and the quality of the optimization results.