Multiobjective Design Optimization of Reentrant Auxetic Model Using Lichtenberg Algorithm Based on Metamodel

Abstract The optimization of five different responses of an auxetic model was considered: mass; critical buckling load under compression effort; natural frequency; Poisson’s ratio; and failure load. The Response Surface Methodology was applied, and a new meta-heuristic of optimization called the Multi-Objective Lichtenberg Algorithm was used to find the optimized configuration of the model. It was possible to increase the failure load by 26,75% in compression performance optimization. Furthermore, in the optimization of modal performance, it was possible to increase the natural frequency by 37.43%. Finally, all 5 responses analyzed simultaneously were optimized. In this case, it was possible to increase the critical buckling load by 42.55%, the failure load by 28.70% and reduce the mass and Poisson’s ratio by 15.97% and 11%, respectively. This paper shows something unprecedented in the literature to date when evaluating in a multi-objective optimization problem, the compression and modal performance of an auxetic reentrant model.