  The human mental search (HMS) algorithm is a relatively recent
population-based metaheuristic algorithm, which has shown competitive
performance in solving complex optimisation problems. It is based on three main
operators: mental search, grouping, and movement. In the original HMS
algorithm, a clustering algorithm is used to group the current population in
order to identify a promising region in search space, while candidate solutions
then move towards the best candidate solution in the promising region. In this
paper, we propose a novel HMS algorithm, HMS-OS, which is based on clustering
in both objective and search space, where clustering in objective space finds a
set of best candidate solutions whose centroid is then also used in updating
the population. For further improvement, HMSOS benefits from an adaptive
selection of the number of mental processes in the mental search operator.
Experimental results on CEC-2017 benchmark functions with dimensionalities of
50 and 100, and in comparison to other optimisation algorithms, indicate that
HMS-OS yields excellent performance, superior to those of other methods.
