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

TL;DR

HMS-OS enhances the human mental search algorithm by incorporating clustering in both search and objective spaces, leading to superior optimization performance on benchmark functions.

Contribution

It introduces a novel clustering approach in both search and objective spaces and an adaptive mechanism for mental process selection in HMS.

Findings

HMS-OS outperforms other algorithms on CEC-2017 benchmarks.

Clustering in both spaces improves search efficiency.

Adaptive mental process selection enhances convergence.

Abstract

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.