MOEA/D with gradient-enhanced kriging for expensive multiobjective optimization

作者：Liu, Fei^*; Zhang, Qingfu; Han, Zhonghua

来源：Natural Computing, 2023, 22(2): 329-339.

DOI：10.1007/s11047-022-09907-0

• Multiobjective optimization
• Expensive optimization
• Surrogate model
• Gradient-enhanced kriging
• Pareto optimality

In many real-world engineering design optimization problems, objective function evaluations are very time costly and often conducted by solving partial differential equations. Gradients of the objective functions can be obtained as a byproduct. Naturally, these problems can be solved more efficiently if gradient information is used. This paper studies how to do expensive multiobjective optimization when gradients are available. We propose a method, called MOEA/D-GEK, which combines MOEA/D and gradient-enhanced kriging. The gradients are used for building kriging models. Experimental studies on a set of test instances and an engineering problem of aerodynamic design optimization for a transonic airfoil show the high efficiency and effectiveness of our proposed method.

• 单位
  西北工业大学