BoostDFO: Improving the performance and moving to newer dimensions in Derivative-Free Optimization

The 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, and resourcing to parallel/cloud computing, new numerical algorithms will be proposed and analyzed. As result, an integrated toolbox for solving multi/single objective, global/local Derivative-free Optimization problems will be available, taking advantage of parallelization and cloud computing, providing an easy access to several efficient and robust algorithms, and allowing to tackle harder Derivative-free Optimization problems.