More advanced techniques have been developed that are suitable for Bayesian optimisation. One method is the bandit greedy-selection approach, which aims to allocate limited resources between competing choices. Bandit methods are often used in conjunction with Thompson sampling, a heuristic algorithm to balance the exploration and exploitation rates in multi-armed bandit problems. The underlying logic is how to choose the most efficient sampling points according to the optimal probability. Ueno et al. Building on these methods, Falkner et al. introduced the Bayesian optimisation and Hy-perBand approach (BOHB). HyperBand aims to speed up the searching process by spreading the initial starting points over different partitions of the parameter space and performing multiple Bayesian optimisation processes at the same time. After several iterations, the worst-performing optimisation processes are stopped, where the fraction stopped is an adjustable hyperparameter, and the other processes allowed to continue, with the process is repeated until there is only one worker left. In their benchmarks, they demonstrated the algorithm exhibits enhanced efficiency throughout the entire learning process and identifies the global minimum with 40 % fewer objective evaluations compared to standard Bayesian optimisation. Furthermore, it allows the utilisation of parallel computing techniques which further decrease the computational time.