An Optimal Sample Allocation Strategy for Partition-Based Random Search

Partition-based random search (PRS) provides a class of effective algorithms for global optimization. In each iteration of a PRS algorithm, the solution space is partitioned into subsets which are randomly sampled and evaluated. One subset is then determined to be the promising subset for further partitioning. In this paper, we propose the problem of allocating samples to each subset so that the samples are utilized most efficiently. Two types of sample allocation problems are discussed, with objectives of maximizing the probability of correctly selecting the promising subset
$(P\{CSPS\})$
given a sample budget and minimizing the required sample size to achieve a satisfied level of
$P\{CSPS\}$
, respectively. An extreme value-based prospectiveness criterion is introduced and an asymptotically optimal solution to the two types of sample allocation problems is developed. The resulting optimal sample allocation strategy (OSAS) is an effective procedure for the existing PRS algorithms by intelligently utilizing the limited computing resources. Numerical tests confirm that OSAS is capable of increasing the
$P\{CSPS\}$
in each iteration and subsequently improving the performance of PRS algorithms.