Engineering design optimization requires an efficient combination of a 3D
shape representation, an optimization algorithm, and a design performance
evaluation method, which is often computationally expensive. We present a
prompt evolution design optimization (PEDO) framework contextualized in a
vehicle design scenario that leverages a vision-language model for penalizing
impractical car designs synthesized by a generative model. The backbone of our
framework is an evolutionary strategy coupled with an optimization objective
function that comprises a physics-based solver and a vision-language model for
practical or functional guidance in the generated car designs. In the prompt
evolutionary search, the optimizer iteratively generates a population of text
prompts, which embed user specifications on the aerodynamic performance and
visual preferences of the 3D car designs. Then, in addition to the
computational fluid dynamics simulations, the pre-trained vision-language model
is used to penalize impractical designs and, thus, foster the evolutionary
algorithm to seek more viable designs. Our investigations on a car design
optimization problem show a wide spread of potential car designs generated at
the early phase of the search, which indicates a good diversity of designs in
the initial populations, and an increase of over 20\% in the probability of
generating practical designs compared to a baseline framework without using a
vision-language model. Visual inspection of the designs against the performance
results demonstrates prompt evolution as a very promising paradigm for finding
novel designs with good optimization performance while providing ease of use in
specifying design specifications and preferences via a natural language
interface.

工程设计优化需要高效结合三维形状表示、优化算法和设计性能评估方法，我们提出了一种在车辆设计场景中利用视觉语言模型来惩罚生成模型合成的不切实际汽车设计的快速演化设计优化 (PEDO) 框架。