Topology optimization of energy absorbers under crashworthiness using modified hybrid cellular automata (MHCA) algorithm

Meta-heuristic and hyperheuristic algorithms are milestones that make the topology optimization practical for dynamic and nonlinear problems. However, researchers are continuing to improve these methods to get better results. Plastic behavior, complex deformation, and load-dependent material properties of the vehicle components during a crash event are challenging issues that are faced in this context. This research focuses on enhancing search efficiency of the hybrid cellular automata (HCA) algorithm with the aim of improving the energy absorption of vehicle structures exposed to high-impact collisions. An attempt is made to utilize an ideal amount of material in the structures to obtain a more uniform distribution of the plastic strain. Thus, the design is based on this criterion throughout the whole structure during the entire collision time. The variable neighborhood radius concept realizes an intelligent search strategy for the modified HCA (MHCA) algorithm. This innovation, applied here to topology optimization design, makes the MHCA algorithm more functional to controlling plastic strain energy. To confirm this, the crash analysis is performed using the finite-element software package LS-DYNA. An additional benefit of using this method is the quick and stable convergence while the energy absorption relative to the mass fraction is remarkably improved.