An Adaptive Parallel EI Infilling Strategy Extended by Non-Parametric PMC Sampling Scheme for Efficient Global Optimization

Abstract

This paper proposes an adaptive parallel Expected Improvement (EI) sampling scheme applied to Efficient Global Optimization, called non-parametric Population Monte Carlo sampling (NPMS) scheme. The NPMS scheme introduces the Population Monte Carlo (PMC) method and the Density-Based Spatial Clustering (DBSCAN) method to address the problem of candidate points aggregation in the parallel EI strategy. In the first stage, samples are uniformly generated from EI function and converge to high EI value sub-domains by PMC iterative succession. A non-parametric sampling method is used to aid PMC convergence and provides valuable sub-domains ranges for the scheme. In the second stage, learning from current potential information, the DBSCAN method is used to cluster the samples and converge to candidate points. During NPMS benchmarking, we verified the feasibility and analyzed the impact of parameters on the scheme. Compared to original EI strategy, NPMS improved EGO minimum results by 14.6% and reduced candidate points by 15.8%. In addition, four test groups are used to compare the NPMS with five other EI-based strategies. Results show that the NPMS scheme has advantages in finding results and saving costs. Mainly, in large input space test group, NPMS saves 34.9% optimization costs and can find better results than parallel strategies. NPMS scheme adaptive sampling provides stable optimization results in the poor prior information, which is expected to provide efficient sampling solutions in more complex problems.