Bi-direction multi-surrogate assisted global optimization

摘要

Purpose - For global optimization, an important issue is a trade-off between exploration and exploitation within limited number of evaluations. Efficient global optimization (EGO) is an important algorithm considering such condition termed as expected improvement (EI). One of major bottlenecks of EGO is to keep the diversity of samples. Recently, Multi-Surrogate EGO uses more samples generated by multiple surrogates to improve the efficiency. However, the total number of samples is commonly large. The purpose of this paper is to suggest a bi-direction multi-surrogate global optimization to overcome this bottleneck.