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Electromagnetic optimization-based clustering algorithm
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DOI：10.1111/exsy.12491.png)
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Abstract
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This paper introduces the electromagnetic clustering algorithm (ELMC), an enhanced variant of electromagnetic field optimization (EFO), for clustering. The motivation behind ELMC is to overcome the shortcomings of traditional k-means clustering algorithm. The performance of k-means primarily depends upon the initial choice of centroids, which can lead the algorithm towards an undesirable local optimum, if chosen incorrectly or inefficiently. The ELMC utilizes the attraction-repulsion concept of the EFO algorithm to maintain the diversity of the population, making it less vulnerable towards the initial choice of centroids. The performance of ELMC is validated on a set of benchmark problems, and the results are compared with other state-of-the-art algorithms. Numerical and graphical results indicate the competence of the proposed ELMC algorithm.
Keywords:
data clustering
electromagnetic field
optimization
meta-heuristic
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