  In this paper, a new sequential surrogate-based optimization (SSBO) algorithm
is developed, which aims to improve the global search ability and local search
efficiency for the global optimization of expensive black-box models. The
proposed method involves three basic sub-criteria to infill new samples
asynchronously to balance the global exploration and local exploitation. First,
to capture the promising possible global optimal region, searching for the
global optimum with genetic algorithm (GA) based on the current surrogate
models of the objective and constraint functions. Second, to infill samples in
the region with sparse samples to improve the global accuracy of the surrogate
models, a grid searching with Latin hypercube sampling (LHS) with the current
surrogate model is adopted to explore the sample space. Third, to accelerate
the local searching efficiency, searching for a local optimum with sequential
quadratic programming (SQP) based on the local surrogate models in the reduced
interval, which involves some samples near the current optimum. When the new
sample is too close to the existing ones, the new sample should be abandoned,
due to the poor additional information. According to the three sub-criteria,
the new samples are placed in the regions which have not been fully explored
and includes the possible global optimum point. When a possible global optimum
point is found, the local searching sub-criterion captures the local optimum
around it rapidly. Numerical and engineering examples are used to verify the
efficiency of the proposed method. The statistical results show that the
proposed method has good global searching ability and efficiency.
