GLOBAL is a multistart type stochastic method for bound constrained global optimization problems. Its goal is to find the best local minima that are potentially global. For this reason it involves a combination of sampling, clustering, and local search. The role of clustering is to reduce the number of local searches by forming groups of points around the local minimizers from a uniform sampled domain and to start few local searches in each of those groups. We evaluate the performance of the GLOBAL algorithm on the BBOB-2009 noiseless testbed, containing problems which reflect the typical difficulties arising in real-word applications. The results show that up to a small function evaluation budget, GLOBAL performs well. We improved the parametrization of it and compared the performance with the MATLAB R2010a GlobalSearch algorithm on the BBOB-2010 noiseless testbed between dimensions 2 and 20. According to the results the studied methods perform similar
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