A parallel optimization algorithm based on FANOVA decomposition

Abstract

The analysis and modeling of complex industrial processes, like the forming of car parts, is often performed with the help of computer simulations. Optimization of such computer experiments usually relies on metamodel-based sequential strategies. The existing sequential algorithms, however, share the limitation that they only allow a single simulation at a time. In this article, we present a very elegant way to produce a parallel optimization procedure, based on a technique from the sensitivity analysis toolbox-the functional analysis of variance graph. The proposed novel simultaneous optimization scheme is called the ParOF algorithm. It is compared with a very effective black-box procedure - the well known efficient global optimization (EGO) algorithm, based on analytical test cases and an optimization study of a sheet forming simulation. Besides demonstrating the advantages of our parallel optimization method, the results show that it can successfully be applied to sheet metal forming for the purpose of quality improvement of the ready parts.