Performance parameters and generative design applications have redefined the human–machine collaborative relationship, challenging traditional architectural design paradigms and guiding the architectural design process toward a performance-based design transformation. This study proposes a multi-objective optimization (MOO) design approach based on performance simulation, utilizing the Grasshopper-EvoMass multi-objective optimization platform. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is applied to coordinate two performance metrics—outdoor thermal comfort and building energy loads—for the multi-objective optimization of architectural design. The results indicate that (1) a performance-based multi-objective optimization design workflow is established. Compared to the baseline design, the optimized building form shows a significant improvement in performance. The Pareto optimal solutions, under 2022 meteorological conditions, demonstrate an annual energy efficiency improvement of 16.55%, and the outdoor thermal neutrality ratio increases by 1.11%. These results suggest that the optimization approach effectively balances building energy loads and outdoor thermal comfort. (2) A total of 1500 building form solutions were generated, from which 16 optimal solutions were selected through the Pareto front method. The resulting Pareto optimal building layouts provide multiple feasible form configurations for the early-stage design phase.

Countries around the world have developed standards for ultra-low energy consumption building design and future plans. Unfortunately, these standards lack specific requirements for industrial heritage. As an important carrier of urban context, history, and the transmission of residents’ memories, industrial heritage cannot be overlooked in urban development. This study uses DesignBuilder energy simulation software to model industrial heritage (taking the Changchun Tractor Factory as an example) and compares the energy consumption before and after renovation strategies. The results show that in the Case 4 plan, after implementing the renovation strategy, heating energy consumption can be reduced by about 11,648 (kWh/m²) over the heating season, the total primary energy was reduced by about 4 million (kgce/tce), and total energy consumption decreases by approximately 95%. This demonstrates the effectiveness of the industrial heritage reuse design strategy proposed in this paper. It provides a new direction for reuse design under ultra-low energy consumption requirements in related case studies.

In the context of global warming and the frequent occurrence of extreme weather, coastal cities are more susceptible to the heat island effect and localized microclimate problems due to the significant influence of the oceanic climate. This study proposes a computer-driven simulation optimization method based on a multi-objective optimization algorithm, combined with tools such as Grasshopper, Ladybug, Honeybee and Wallacei, to provide scientific optimization decision intervals for morphology control and evaluation factors at the initial stage of coastal city block design. The effectiveness of this optimization strategy is verified through empirical research on typical coastal neighborhoods in Dalian. The results show that the strategy derived from the multi-objective optimization-based evaluation significantly improves the wind environment and thermal comfort of Dalian neighborhoods in winter and summer: the optimization reduced the average wind speed inside the block by 0.47 m/s and increased the UTCI by 0.48 °C in winter, and it increased the wind speed to 1.5 m/s and decreased the UTCI by 0.59 °C in summer. This study shows that the use of simulation assessment and multi-objective optimization technology to adjust the block form of coastal cities can effectively improve the seasonal wind and heat environment and provide a scientific basis for the design and renewal of coastal cities.

Vernacular settlements in hot–humid regions preserve climate-responsive spatial knowledge, yet evidence on how linked outdoor, transitional, and indoor spaces jointly shape microclimate and thermal comfort remains limited. This study investigates a compact Hakka settlement in southern Jiangxi, China, by integrating field measurements, calibrated simulation, PET-based thermal-comfort assessment, and parametric scenario comparison to examine microclimatic differentiation across cold alleys, patios, halls, semi-open interfaces, and interior rooms. The results reveal clear microclimatic gradients across the linked vernacular spatial sequence. During the summer afternoon peak, cold alleys reduced air temperature by approximately 2.5 °C and PET by approximately 8.5 °C relative to ordinary streets, while semi-enclosed spaces adjacent to patios reduced air temperature by approximately 4.0 °C but increased relative humidity by 8–12%, indicating a cooling–moisture trade-off. Measured and simulated air temperature and wind speed showed satisfactory agreement and reproduced the main thermal and ventilation hierarchy across the connected spaces. Parametric comparison further identified case-based geometry-performance tendencies under the tested boundary conditions: within the tested cold-alley scenarios, widths of approximately 0.8–1.4 m combined with an H/W ratio close to 3:1 showed relatively favorable airflow-temperature performance in terms of shading continuity, moderated airflow, and reduced summer thermal exposure. The findings suggest that thermal comfort in compact hot–humid vernacular settlements depends on radiant-load reduction, moderated ventilation, and thermal buffering rather than on ventilation enhancement alone. Beyond the case-specific evidence, this study contributes a sequence-based, locally calibratable approach for preliminary retrofit appraisal in comparable compact hot–humid vernacular settlements.