基于Kriging代理模型的约束差分进化算法

High-fidelity analysis models have been widely used in modern design, significantly increasing the computational budget of engineering design optimization. To reduce the computational cost, researchers have paid extensive attention to Surrogate Assisted Evolutionary Algorithms (SAEAs) recently. A Kriging-assisted Constrained Differential Evolution algorithm (KRG-CDE) is developed in this study to improve the efficiency of SAEAs in solving constrained optimization problems. Based on constraint improvement probability and optimality fitness, an improved feasibility rule is tailored to enhance the potential optimality and feasibility of the infill sample points. Moreover, the global exploration and local exploitation capacity of the KRG-CDE are balanced according to the population improvement. The proposed method is tested on several standard benchmark problems and compared with global and local surrogate-assisted differential evolution and (μ+λ)-constrained differential evolution to verify its optimization performance. The comparison results illustrate that the KRG-CDE outperforms the competitors in terms of efficiency, convergence, and robustness. Finally, the KRG-CDE is successfully applied to an all-electric propulsion satellite multidisciplinary design optimization problem, demonstrating the practicality and effectiveness of the proposed KRG-CDE in engineering practices.