Abstract As the global ‘dual carbon’ goals advance progressively, the construction industry, a key energy-consuming sector, has made improving energy efficiency central to achieving green and sustainable development. Yet architectural design faces growing challenges in striking a balance among aesthetics, functionality, and energy efficiency: traditional design methods remain heavily experience dependent and lack systematic quantitative performance analysis; particularly in optimizing solar–thermal performance, they frequently overlook the in-depth impact of building form on energy efficiency. Against this backdrop, this study presents a multiobjective optimization-based framework for generating solar–thermal collaborative building forms, aimed at realizing automated building form evolution and in-depth collaborative optimization of solar–thermal performance via algorithm-driven generative design. Specifically, it develops a mathematical model rooted in photothermal objective functions, integrates generative design with multiobjective optimization, and leverages physical simulation engines to simulate dynamic energy performance. Research findings show that the proposed framework delivers substantial breakthroughs across multiple dimensions: its conclusions effectively reconcile the complex interplay between solar–thermal performance and building form, overcoming the limitations of traditional ‘single-objective optimization’ design logic and holding notable theoretical value. Moreover, this approach offers novel insights for architectural design and provides robust technical support for future green building practices.
Danqiong Liu (2026) studied this question.