Design Optimization of a Vehicle B-Pillar Subjected to Roof Crush Using Mixed Reactive Taboo Search

Abstract

The primary obstacle in automated design for crashworthiness is the heavy computational resources required during the optimization processes. Hence it is desirable to develop efficient optimization algorithms capable of finding good solutions without requiring too many model simulations. This paper presents an efficient mixed discrete and continuous optimization algorithm, Mixed Reactive Taboo Search (MRTS), and its application to the design of a vehicle B-Pillar subjected to roof crush conditions. The problem is sophisticated enough to explore the MRTS' capability of identifying multiple local optima with a single optimization run, yet the associated finite element model (FEM) is not too large to make the computational resources required for global optimization prohibitive. The optimization results demonstrated that a single run of MRTS identified a set of better designs with smaller number of simulation runs, than multiple runs of Sequential Quadratic Programming (SQP) with several starting points.