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

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, HMS-OS 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.

I. Introduction

Global optimisation algorithms aim to find the best among all feasible solutions for a given problem. Conventional algorithms such as gradient-based approaches are popular yet ineffective and sometimes impractical [1], and also suffer from some difficulties such as their tendency towards local optima and requirements to calculate derivative information. Population-based metaheuristic algorithms, such as particle swarm optimisation [2], can provide a reliable alternative.