An improved mode-pursing sampling method that balances global exploration and local exploitation based on kriging
Mingyang Li, Wenlong Liu · Engineering Optimization · 2021
Mode-pursing sampling (MPS) is an efficient approach for solving expensive optimization problems. However, MPS does not actively seek sample points while considering the error information of the surrogate model. Hence, an improved MPS based on kriging (IMSK) is proposed in this article. This method employs the probability improvement function as a probabilistic distribution function and obtains multiple sampling points with a certain probability from a candidate set composed of the exploitation–exploration trade-off points in each iteration. In addition, two acceleration strategies are proposed to speed up the sequential optimization process. Several typical benchmark functions and an engineering problem are applied to test the performance of IMSK and compare it to that of different versions of MPS. The results show that IMSK can provide good optimized solutions at the same or lower computing costs. These properties may make IMSK suitable for addressing actual engineering optimization problems.