| Summary: | This dissertation is framed on the ongoing research project BoostDFO - Improving the performance and moving to newer dimensions in Derivative-Free Optimization. The final goal of this project is to develop efficient and robust algorithms for Global and/or Multiobjective Derivative-free Optimization. This type of optimization is typically required in complex scientific/industrial applications, where the function evaluation is time-consuming and derivatives are not available for use, neither can be numerically approximated. Often problems present several conflicting objectives or users aspire to obtain global solutions. Inspired by successful approaches used in single objective local Derivative-free Optimization, we intend to address the inherent problem of the huge execution times by resorting to parallel/cloud computing and carrying a detailed performance analysis. As result, an integrated toolbox for solving single/multi objective, local/global Derivativefree Optimization problems is made available, with recommendations for taking advantage of parallelization and cloud computing, providing easy access to several efficient and robust algorithms and allowing to tackle harder Derivative-free Optimization problems.
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