Exploiting linear interpolation of variational autoencoders for satisfying preferences in evolutionary design optimization

In the early design phase of automotive digital development, one of the key challenges for the designer is to consider multiple-criteria like aerodynamics and structural efficiency besides aesthetic aspects for designing a car shape. In our research, we imagine a cooperative design system in the automotive domain which provides guidance to the designer for finding sets of design options or well-performing designs for preferred search areas. In the present paper, we focus on two perspectives for this multi-criteria decision-making problem: First, a scenario without prior information about design preferences, where the designer aims to explore the search space for a diverse set of design alternatives. Second, a scenario where the designer has a prior intuition on preferred solutions of interest. For both scenarios, we assume that historic 3D car shape data exists, which we can utilize to learn a compact low-dimensional design representation based on a variational autoencoder (VAE). In contrast to evolutionary multi-objective optimization approaches where starting populations are randomly initialized, we propose to seed the population more efficiently by exploiting the advantage of linear interpolation in the latent space of the VAE. In our experiments, we demonstrate that the multi-objective optimization converges faster and achieves a diverse set of solutions. For the second scenario, when specifying design preferences by weights, we improve on the weighted-sum method, which simplifies the multi-objective problem and propose a strategy for efficiently adapting the weights towards the preferred design solution.