A novel batch-selection strategy for parallel global optimization

Bayesian-based parallel global optimization methods have become increasingly popular for solving black-box functions using parallel resources. When the function exhibits multimodality, it is difficult to identify the global optimum. This article proposes a novel batch selection strategy for parallel global optimization called Sampling-Optimizing-Selecting (SOS) to address this problem. SOS, utilizing minimum energy design, gradient optimization and hypersphere clustering, can adaptively explore multiple local optimal regions of the objective function simultaneously and make full use of parallel resources to obtain the global optimum efficiently. Additionally, the SOS algorithm maintains the sparsity between sample points to a certain extent, which enhances the accuracy of the surrogate model and thus improves the robustness of the optimization effect. Several numerical simulations and optimization experiments involving array decoy jamming are presented to demonstrate the superiority of the proposed strategy.