Kriging-based unconstrained global optimization through multi-point sampling

Abstract

ABSTRACTThe generalized efficient global optimization (GEGO) method is able to solve expensive black-box problems. However, selecting one sampling point per cycle may result in large time consumption and loss of convergence accuracy. To this end, a kriging-based multi-point unconstrained global optimization (KMUGO) method is proposed. It extends the GEGO method with the improved constant liar (CL) strategy. For each cycle, the kriging model is first constructed or updated by the existing sampled data. Then, the enhanced alternative CL strategy is used to find multiple points, which will be further screened to identify the final expensive-evaluation points. Test results of numerical problems and an engineering simulation case show that KMUGO can deliver better convergence than GEGO, multi-point sampling method-based kriging (MPSK), hybrid and adaptive meta-model-based global optimization (HAM) and the kriging-based global optimization method using multi-point infill search criterion (KGOMISC).