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The constrained global optimization problem is addressed using two relatively new local constrained optimization algorithms, namely the GLS1C and the Dynamic-Q algorithms. Both have relatively good global convergence capabilities. They are used in a multi-start strategy in combination with a Bayesian global stopping criterion. The suitability of the Bayesian stopping criterion is demonstrated for...
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