Abstract
In Part II of our paper, two stochastic methods for global optimization are described that, with probability 1, find all relevant local minima of the objective function with the smallest possible number of local searches. The computational performance of these methods is examined both analytically and empirically.
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Rinnooy Kan, A.H.G., Timmer, G.T. Stochastic global optimization methods part II: Multi level methods. Mathematical Programming 39, 57–78 (1987). https://doi.org/10.1007/BF02592071
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DOI: https://doi.org/10.1007/BF02592071