How bad are the BFGS and DFP methods when the objective function is quadratic?
Mathematical Programming: Series A and B
Stochastic global optimization methods. part 1: clustering methods
Mathematical Programming: Series A and B
Stochastic global optimization methods. part 11: multi level methods
Mathematical Programming: Series A and B
Bayesian stopping rules for multistart global optimization methods
Mathematical Programming: Series A and B
Optimal and sub-optimal stopping rules for the Multistart algorithm in global optimization
Mathematical Programming: Series A and B
Global Optimization by Multilevel Coordinate Search
Journal of Global Optimization
Scatter Search and Local NLP Solvers: A Multistart Framework for Global Optimization
INFORMS Journal on Computing
Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Evaluating las vegas algorithms: pitfalls and remedies
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Benchmarking a hybrid multi level single linkagealgorithm on the bbob noiseless testbed
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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GLOBAL is a multi-start 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 uniformly 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-world applications. The obtained results are also compared with those obtained form the simple multi-start procedure in order to analyze the effects of the applied clustering rule. An improved parameterization is introduced in the GLOBAL method and the performance of the new procedure is compared with the performance of the MATLAB GlobalSearch solver by using the BBOB 2010 test environment.