标题： 评估多目标顺序优化方法在建筑性能设计中的有效性、可靠性和效率 显示英文标题

标题： Evaluating the effectiveness, reliability and efficiency of a multi-objective sequential optimization approach for building performance design

摘要： 基于性能的建筑设计的复杂性源于对大量候选设计方案的评估，这些设计受到多学科项目中固有的变量、目标和约束的驱动。 这需要优化方法来支持识别表现良好的设计，同时减少性能评估的计算时间。 为此，本文提出并评估了一种用于建筑几何、围护结构、暖通空调系统和控制的多目标设计优化的顺序方法。 该方法涉及顺序优化，将前一阶段的最优解传递到下一阶段。 将顺序方法的性能与全因子搜索进行比较，评估其在寻找全局最优解、解决方案质量、可扩展性和问题公式变化的可靠性以及与NSGA-II算法相比的计算效率方面的有效性。 在多尺度案例研究中测试了顺序方法的24种配置，模拟了874至4,147,200种设计方案，针对办公楼，旨在最小化能源需求的同时保持热舒适性。 两阶段顺序过程——(建筑几何+围护结构)和(HVAC系统+控制)在所有四个尺度和问题公式的变化中找到了与全因子搜索相同的帕累托最优解，证明了其100%的有效性和可靠性。 这种方法需要100,700次函数评估，与全因子搜索相比，计算工作量减少了91.2%。 相比之下，NSGA-II在相同数量的函数评估下仅达到了73.5%的全局最优解。 这项研究表明，顺序优化方法是标准NSGA-II算法的一种高效且稳健的替代方法。 显示英文摘要

摘要： The complexity of performance-based building design stems from the evaluation of numerous candidate design options, driven by the plethora of variables, objectives, and constraints inherent in multi-disciplinary projects. This necessitates optimization approaches to support the identification of well performing designs while reducing the computational time of performance evaluation. In response, this paper proposes and evaluates a sequential approach for multi-objective design optimization of building geometry, fabric, HVAC system and controls for building performance. This approach involves sequential optimizations with optimal solutions from previous stages passed to the next. The performance of the sequential approach is benchmarked against a full factorial search, assessing its effectiveness in finding global optima, solution quality, reliability to scale and variations of problem formulations, and computational efficiency compared to the NSGA-II algorithm. 24 configurations of the sequential approach are tested on a multi-scale case study, simulating 874 to 4,147,200 design options for an office building, aiming to minimize energy demand while maintaining thermal comfort. A two-stage sequential process-(building geometry + fabric) and (HVAC system + controls) identified the same Pareto-optimal solutions as the full factorial search across all four scales and variations of problem formulations, demonstrating 100% effectiveness and reliability. This approach required 100,700 function evaluations, representing a 91.2% reduction in computational effort compared to the full factorial search. In contrast, NSGA-II achieved only 73.5% of the global optima with the same number of function evaluations. This research indicates that a sequential optimization approach is a highly efficient and robust alternative to the standard NSGA-II algorithm.