 Adaptive black widow optimisation algorithm for data clusteringAnmar Abuhamdah International Journal of Mathematics in Operational Research, 2021, vol. 20, issue 2, 239-263 Abstract: Generally, local search approaches are better than population-based approaches in exploiting the search space and worse for exploration. Recently, the black widow optimisation (BWO) algorithm was proposed for engineering optimisation problems as an algorithm balancing between the exploration and exploitation phases. However, the BWO algorithm employed three essential parameter rates, in which a different experiment is needed for each problem. The adaptive black widow optimisation (ABWO) algorithm is proposed to tune the parameters adaptively and accept worse solutions by relying on a local search, using the solutions' qualities average. Six medical datasets are used as a test domain with two calculations criteria to calculate the minimal distance. In order to evaluate the effectiveness of ABWO, a comparison is made between ABWO, BWO and other methods' performances that have been drawn from the acknowledged literature. Outcomes show ABWO is capable of obtaining better cluster qualities, thus outperforming many other methods. Keywords: medical clustering problem; distance function; population-based algorithm; black widow optimisation algorithm; adaptive. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijmore:v:20:y:2021:i:2:p:239-263 Access Statistics for this article More articles in International Journal of Mathematics in Operational Research from Inderscience Enterprises Ltd |
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