The Impact of Different Parallel Strategies on the Performance of Kriging-Based Efficient Global Optimization Algorithms

A parallel efficient global optimization (EGO) algorithm with a pseudo expected improvement (PEI) multi-point sampling criterion, proposed in recent years, is developed to adapt the capabilities of modern parallel computing power. However, a comprehensive and clear discussion on the impact of different point-filling strategies on the optimization performance of the parallel EGO algorithm is still lacking, limiting its theoretical reference for practical applications and technological advancements. To address this gap, this study comprehensively investigates the optimization performance of the parallel EGO algorithm based on the PEI multi-point sampling criterion by analyzing the impact of different point-filling strategies under kriging surrogate models of varying fidelity. Therefore, nine benchmark test functions with different optimization problem characteristics were selected as optimization test objects, and the results were systematically analyzed from the perspectives of convergence performance, optimization efficiency, and algorithmic diversity. The analysis results indicate that the higher-fidelity kriging surrogate model enhances the stability of the parallel EGO algorithm in terms of convergence performance, optimization efficiency, and algorithmic diversity.