An efficient global optimization method with multi-point infill sampling based on kriging
Mingyang Li, Bing Yi, Yueting Yang · Engineering Optimization · 2021
In general, infill sampling is the core process of efficient global optimization (EGO). Research on infill sampling with few points, high convergence speed and simplicity has received increasing attention in recent years. In this article, an EGO method with multi-point infill sampling based on kriging (MISK) is proposed. This method obtains multiple sampling points in each iteration by solving a multi-objective problem (MOP). This method can intelligently select points that balance global exploration and local exploitation. In addition, two sampling functions are proposed to construct the MOP. The results show that compared with other well-known global optimization methods, the MISK algorithm can obtain good optimization solutions in a shorter amount of time and that it requires fewer computing resources with extremely few parameters, which may meet the needs of real-world engineering optimization problems.