Title: MinCentre: using clustering in global optimisation

Authors: Vasileios Charilogis; Ioannis G. Tsoulos

Addresses: Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, Kostakioi, GR, 47100, Arta, Greece ' Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, Kostakioi, GR, 47100, Arta, Greece

Abstract: A common problem that arises in many scientific fields is locating the global minimum of a multi-modal function. A novel clustering technique that tackles this problem is introduced here. The proposed method creates clusters from uniform samples of the objective function with the usage of the K-means clustering technique. For every cluster a centre is created. Finally, a simple rejection procedure is applied to the created clusters in order to remove clusters that are close to others. The proposed method is tested on a series of well-known optimisation problems from the relevant literature and the results are reported and compared against the simple multi-start global optimisation method.

Keywords: global optimisation; clustering; hybrid methods; numerical methods.

DOI: 10.1504/IJCISTUDIES.2022.1004245

International Journal of Computational Intelligence Studies, 2022 Vol.11 No.1, pp.24 - 35

Received: 07 May 2021
Accepted: 31 May 2021

Published online: 07 Jun 2022 *

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