Abstract
In this stochastic approach to global optimization, clustering techniques are applied to identify local minima of a real valued objective function that are potentially global. Three different methods of this type are described; their accuracy and efficiency are analyzed in detail.
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Rinnooy Kan, A.H.G., Timmer, G.T. Stochastic global optimization methods part I: Clustering methods. Mathematical Programming 39, 27–56 (1987). https://doi.org/10.1007/BF02592070
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DOI: https://doi.org/10.1007/BF02592070