Intelligent design exploration method for complex engineered system architecture generation

Owing to the discontinuous specificity and complexity of architecture design space, the issue of selecting and combining components comprising the engineered system, numerous constraints and associations need to be accounted for, adding up to a complex and substantial cognitive load on the system architects, which makes it challenging to tackle the current demand of adaptive improvements or innovative upgrading of the existing mature architectural solutions. To this end, this paper proposes an intelligent design exploration method for complex system architecture generation with reinforcement learning. The architectural design space (ADS) is identified by defining the dimensions of ADS, including model, quantity, and design chain, as well as the mathematical boundaries and representation to facilitate computable intelligent design exploration. On this basis, by adopting AI techniques primarily based on reinforcement learning, a massive and reliable architectural scheme is rapidly generated, and a more satisfying and robust architectural solution is selected by accessing the fuzzy Pareto frontier. Validation of the method is demonstrated through a case study of a launch vehicle's first and second-stage separation system. This research contributes valuable insights to overcoming the limitations of traditional techniques and enhancing the efficiency of the generative design and decision-making for complex engineered system architecture.