MCS-HMS: A Multi-Cluster Selection Strategy for the Human Mental Search Algorithm
This is an incremental improvement for global optimization in population-based metaheuristics.
The paper tackled the problem of the Human Mental Search (HMS) algorithm being time-consuming and having poor exploration by proposing MCS-HMS, which uses best bids from multiple clusters and a one-step k-means for clustering, resulting in outperformance over HMS and other algorithms.
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