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.

翻译：人类心理搜索算法(HMS)是一种相对较近的基于人口的计量算法,它显示在解决复杂的优化问题方面的竞争性表现,它基于三个主要操作者:精神搜索、分组和运动。在最初的HMS算法中,群集算法用于对当前人口进行分组,以便确定有希望的搜索空间区域,而候选解决方案随后在有希望的区域转向最佳的候选解决方案。在本文件中,我们提议一种新型的HMS算法(HMS-OS),该算法以客观和搜索空间的组合为基础,在客观空间中集成找到一套最佳的候选解决方案,其中间体随后也用于更新人口。为了进一步改进,HMSOS得益于对精神搜索操作者中精神过程数量的适应性选择。CEC-2017基准功能的实验结果有50和100维度,与其他优化算法相比,显示HMS-OS的性优于其他方法。