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The human mental search (HMS) algorithm is a relatively recentpopulation-based metaheuristic algorithm, which has shown competitiveperformance in solving complex optimisation problems. It is based on three mainoperators: mental search, grouping, and movement. In the original HMSalgorithm, a clustering algorithm is used to group the current population inorder to identify a promising region in search space, while candidate solutionsthen move towards the best candidate solution in the promising region. In thispaper, we propose a novel HMS algorithm, HMS-OS, which is based on clusteringin both objective and search space, where clustering in objective space finds aset of best candidate solutions whose centroid is then also used in updatingthe population. For further improvement, HMSOS benefits from an adaptiveselection of the number of mental processes in the mental search operator.Experimental results on CEC-2017 benchmark functions with dimensionalities of50 and 100, and in comparison to other optimisation algorithms, indicate thatHMS-OS yields excellent performance, superior to those of other methods.

HMS-OS: Improving the Human Mental Search Optimisation Algorithm by Grouping in both Search and Objective Space