对既有建筑进行节能改造是减少建筑业能源消耗和碳排放的重要策略之一，提出了BP神经网络与蒙特卡洛-非支配排序遗传算法（Monte Carlo-non-dominated ranking genetic algorithm, MC-NSGA Ⅲ）相结合的多目标优化方法，对建筑围护结构改造参数进行优化设计。基于DesignBuilder软件进行建筑性能模拟，得到样本数据集；利用BP神经网络学习数据集，建立建筑围护结构与性能指标之间的预测模型，作为各个目标的适应度函数；利用蒙特卡洛方法对交叉概率和变异概率进行不确定性分析，建立MC-NSGA Ⅲ多目标优化模型，得到Pareto最优解集；最后利用理想点法找到围护结构设计参数的最优组合。以科教综合楼为例，验证了该方法的可行性和有效性。结果表明，提出的方法可在多种改造设计方案中找到一个综合权衡的最优方案，研究结果可为建筑改造规划与设计提供参考。

Building retrofit is an important strategy to reduce energy consumption and carbon emission in building industry. In order to optimize the design of building envelope retrofit, a multi-objective optimization method combining BP neural network and Monte Carlo-non-dominated ranking genetic algorithm (MC-NSGA Ⅲ) was proposed. The DesignBuilder software was utilized for building performance simulation to obtain sample data. The BP neural network was utilized to establish prediction models between building envelope and building performance. The prediction models were used as the fitness function for each objective. Monte Carlo method was used for uncertainty analysis of crossover and variation probabilities. The MC-NSGA Ⅲ multi-objective optimization model was constructed to obtain the Pareto front. Then ideal point method was utilized to discover the optimal parameters combination. A case study of a school building in China was used to demonstrate the feasibility and effectiveness. The results indicate that the proposed method can find a comprehensive trade-off solution and provide references for building retrofit planning and design.