Analysis of the Bayesian multi-scale optimistic optimization on the CEC2016 and BBOB testbeds

This paper provides an empirical analysis of the recently proposed Bayesian Multi-Scale Optimistic Optimization (BaMSOO) algorithm for solving bound-constrained black-box global optimization problems under expensive as well as cheap budgets of function evaluations. The CEC2016 and BBOB benchmarks are used in assessing the algorithm. We also compare BaMSOO with the Simultaneous Optimistic Optimization (SOO) algorithm from which BaMSOO is derived. The results indicate that BaMSOO has a comparable performance with SOO under expensive-budget settings but outperforms it with increasing evaluation budgets. Finally, we provide useful insights regarding the algorithm's relative efficiency and future directions.