Comparison of Global Optimization Methods for Drag Reduction in the Automotive Industry

摘要

Various global optimization methods are compared in order to find the best strategy to solve realistic drag reduction problems in the automotive industry. All the methods consist in improving classical genetic algorithms, either by coupling them with a deterministic descent method or by incorporating a fast but approximated evaluation process. The efficiency of these methods (called HM and AGA respectively) is shown and compared, first on analytical test functions, then on a drag reduction problem where the computational time of a GA is reduced by a factor up to 7.