## 1 Introduction
[17], with the object of an independent two-story house in Argentina, selected twelve parameters as variables, including building orientation, roof type, interior and exterior wall type, exterior window size and type, and exterior shading scale, and adopted the method of coupling NSGA-II and ANN algorithms to obtain the Pareto-optimal solution set of design parameters. [PERSON] et al. [18], based on Grasshopper's Honeybee simulation engine and Octopus optimization module, carried out multi-objective optimization for nine shape parameters of rural houses in Tianjin, and they applied the TOPSIS method to obtain the final design scheme. With respect to the research of weighing buildings' energy consumption and economic aspects, [PERSON] et al. [19] combined the MOBO optimization engine and IDA-ICE 5.0 simulation software to optimize the life-cycle energy consumption and cost for detached houses in Finland, and they obtained a non-dominated solution set, including the insulation layer thickness of external walls, roofs, and floors, as well as the external window type. [PERSON] et al. [20], taking houses in France and Lebanon as examples, coupled the MOBO and TRNSYS software to carry out optimization research for life-cycle cost and operating load, with the variables of insulation layer thickness of external walls and roofs, external window type, heating and cooling temperature, and the number of solar collectors and photovoltaic panels. [PERSON] et al. [21] carried out a multi-objective optimization of economy and energy efficiency for high-rise buildings in five cities of various climate zones by joining the NSGA-II algorithm and TRNSYS (V 16) software, and they obtained the Pareto solution set, including the variables of building envelope heat transfer coefficient, total heat recovery efficiency, etc. In terms of optimizing costs and carbon emissions, [PERSON] et al. [22] used an ANN and the NSGA-IIalgorithm to obtain the solution set of variables, optimizing the life-cycle cost and carbon emissions for high-rise residential buildings in Harbin. Further research has led to the scope of multi-objective optimization expanding to three-objective optimization or more. For example, [PERSON] et al. [23] adopted the NSGA-II, MOPSO, MOGA, and MODE algorithms to carry out their optimization study for residential buildings in Nanjing, with the objectives of life-cycle cost, carbon emissions, and thermal comfort, as well as the variables of sixteen relevant parameters. [PERSON] et al. [24], taking five-story houses in Italy as examples, implemented a GA by means of coupling between MATLAB and EnergyPlus to minimize primary energy consumption, global cost, and discomfort hours. The design variables concerned the set-point temperatures, the radiative properties of plasters, the thermophysical properties of envelope components, window type, and building orientation. [PERSON] et al. [25], targeting a typical collective residential building (four floors) in Morocco, carried out multi-objective optimization by coupling MOBO and TRNSYS software to find the best solution that allows a compromise between the building life-cycle cost, energy saving, and thermal comfort. Building design variables and RE system variables were used during optimization. [PERSON] et al. [26] used the combination of an ANN prediction model and the NSGA-II algorithm for multistory houses in South Korea to optimize building energy demand, life-cycle assessment, and life-cycle cost, and then, they obtained the Pareto-optimal solutions with twelve variables. [PERSON] et al. [27] established an MBC-BPNN prediction model with the objectives of optimizing the heating energy consumption, building envelope cost, and PMV of grassland dwellings in western Inner Mongolia. With this model, multi-objective optimization was conducted for the building envelope and window-wall ratio. [PERSON] et al.