Good energy performance is very important for decreasing the consumption of energy-intensive buildings. This paper studies a new multi-objective evolutionary approach to optimize the building energy performance. Firstly, the problem is modeled as two-objective optimization functions, i.e., minimizing the energy consumption and maximizing the comfort level. Next, a new solving algorithm based on bare-bones particle swarm optimization is developed. In the algorithm, an improved update strategy with adaptive perturbation is proposed to overcome the disadvantage of easily local convergence of the traditional algorithm. The algorithm deletes control parameters including inertia weight and acceleration coefficients from the traditional algorithm, showing the advantage of easy-to-use. Applying the proposed algorithm into several buildings located in China, experiment result shows that the proposed algorithm can obtain good non-dominated solutions, which is obviously better than that of those compared algorithms. The uncomfortable hours of the proposed algorithm on part cases reduce 11.82%, but its energy consumption only increases 1.74%. All indicate that the proposed algorithm is a powerful tool for optimizing building energy preference.

良好的能源性能对于减少能源密集型建筑物的消耗非常重要。本文研究了一种新的多目标进化方法，以优化建筑的能源性能。首先，将问题建模为两个目标的优化函数，即最小化能耗和最大化舒适度。接下来，开发了一种基于准粒子群优化的新求解算法。在该算法中，提出了一种改进的具有自适应扰动的更新策略，以克服传统算法容易局部收敛的缺点。该算法从传统算法中删除了包括惯性权重和加速度系数在内的控制参数，显示了易于使用的优势。将所提算法应用于我国几座建筑物中，实验结果表明，所提算法能够获得良好的非支配解，明显优于所比较的算法。该算法在部分情况下的不舒服时间减少了11.82％，但其能耗仅增加了1.74％。所有这些都表明，所提出的算法是优化建筑物能源偏好的有力工具。