A framework of global exploration and local exploitation using surrogates for expensive optimization

In solving expensive optimization problems, most research focuses on using either a single global surrogate or multiple local surrogates in surrogate-assisted evolutionary algorithms (SAEAs) or evolutionary Bayesian optimization (EBO). It is challenging for single global or multiple local surrogates to balance the exploration and exploitation of algorithms. To address this issue, the paper proposes a framework of global exploration and local exploitation with surrogates for solving expensive optimization problems. In detail, a scalable Gaussian process (GP) is used to fit the global fitness landscape and guide algorithms towards global exploration. A radial basis function network (RBFN) guides algorithms for local exploitation by capturing the local patterns identified by the global surrogate. In the local exploitation, a local search region is proposed to ensure that the search region contains the bilateral region of the current optimum solution. This way, the exploration and exploitation of algorithms can be balanced cooperatively by global and local surrogates. The effectiveness of the proposed method has been investigated and analyzed on a set of expensive benchmark problems.