Population-based metaheuristic algorithms have received significant attention in global optimisation. Human Mental Search (HMS) is a relatively recent population-based metaheuristic that has been shown to work well in comparison to other algorithms. However, HMS is time-consuming and suffers from relatively poor exploration. Having clustered the candidate solutions, HMS selects a winner cluster with the best mean objective function. This is not necessarily the best criterion to choose the winner group and limits the exploration ability of the algorithm. In this paper, we propose an improvement to the HMS algorithm in which the best bids from multiple clusters are used to benefit from enhanced exploration. We also use a one-step k-means algorithm in the clustering phase to improve the speed of the algorithm. Our experimental results show that MCS-HMS outperforms HMS as well as other population-based metaheuristic algorithms

翻译：在全球优化中,以人口为基础的计量经济学算法已经在全球优化中受到极大关注。人类心理搜索(HMS)是一个较近的、以人口为基础的计量经济学,与其他算法相比,已经证明与其他算法相比效果良好。然而,HMS是耗时的,而且探索也比较差。在对候选解决方案进行分组后,HMS选择了一个具有最佳平均客观功能的优胜者组。这并不一定是选择优胜者组的最佳标准,也限制了该算法的探索能力。在本文中,我们建议改进HMS算法,利用多个组群的最佳出价从强化的勘探中获益。我们还在集群阶段使用一步K方法算法来提高算法的速度。我们的实验结果表明,MCS-HMS优于HMS以及其他以人口为基础的计量算法。