MCS-HMS: A Multi-Cluster Selection Strategy for the Human Mental Search Algorithm

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 re...

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Vydáno v:2021 IEEE Symposium Series on Computational Intelligence (SSCI) s. 1 - 6
Hlavní autoři: Bojnordi, Ehsan, Mousavirad, Seyed Jalaleddin, Schaefer, Gerald, Korovin, Iakov
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 05.12.2021
Témata:
clustering
Clustering algorithms
Computational intelligence
Global optimisation
Human Mental Search
Linear programming
metaheuristic algorithms
Metaheuristics
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Shrnutí: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.
DOI:10.1109/SSCI50451.2021.9660143