# Study on Multi-Objective Optimization-Based Climate Responsive Design of Residential Building
## 2 Background and Literature
### State-of-the-Art  
There are many scholars in the field of building optimization by integrating rhino, grasshopper (GH), building performance simulation plug-ins (such as DIVA) and GH evolution solver: Galapagos, to conduct building performance optimization, including building energy-efficient skin optimization, high performance building system optimization, building orientation optimization, building operations optimization, life cycle assessment and alternative energy applications [8; 9; 10; 11], etc. However, in the GH platform, Galapagos can only optimize one objective function at a time, so when dealing with architectural multi-objective optimization problems, it is necessary to reprocess the data results, or use other evolutionary solvers of the platform, such as Octopus. For the multi-objective building optimization, [PERSON] et al. [12] proposed another operation process, that is, using TRNSYS, GenOpt, and Tchebycheff optimization technology developed in MATLAB to simulate and optimize the building environment. [PERSON] et al. [13] also explored Revit's plug-in Dynamo to extend the parametric functions of the platform. They also used NSGA-II's free software package Optimo to solve optimization problems. In terms of research on building renovation and design by multi-objective optimization methods, [PERSON] et al. [14] defined the decision variables and their ranges based on the lowest building energy consumption and the lowest investment cost to achieve the best combination of renovation parameters. The strategies mentioned in this study are common measures for building renovation, such as the thermal insulation performance of external walls and windows, the size of windows and the lighting effect of glass, etc., which are convenient for large-scale popularization and application. [PERSON] et al. [15] investigated the open space of office buildings, including location, shape, window type and thickness of masonry walls as decision variables, using EnergyPlus and NSGA-II algorithm (Non-dominated Sorting Genetic Algorithm) for building environment simulation and multi-objective optimization to search Pareto frontier for building energy efficiency design. [PERSON] and [PERSON] et al. [16; 17] discussed the robustness of the optimal solution obtained by GA multi-objective optimization to the quality of the weather data used. Using the climate parameters of six different reference years in Trento and Monza in northern Italy, they applied four energy-saving measures related to building envelopes and HVAC systems to six typical building types, and studied when to adopt NSGA -II Genetic algorithm selects the most cost-effective building energy-saving renovation measures, to what extent the uncertainty of typical weather conditions will affect the results of building energy-saving renovation and TRNSYS simulation. [PERSON] et al. [18] evaluated the optimal combination of building energy efficiency measures (EEM) by using multi-objective optimization algorithms and dynamic simulation tools to achieve the results of economic optimization, minimum energy consumption and maximum thermal environment comfort. Figure 2: Deterministic forward simulation (**a**), probabilistic forward modeling (**b**), probabilistic reverse simulation workflow (**c**). Based on the perspective of architectural design, the research on the climatic responsive design of residential buildings presents a trend of gradually deepening and refining over time, which is reflected in the following characteristics:  
1. From energy-saving design practice or theoretical research based on qualitative analysis to energy consumption simulation based on quantitative research. 2. Research related to building energy consumption is becoming more and more comprehensive, from only focusing on building thermal performance or energy consumption of air conditioning systems to a comprehensive evaluation system that also considers other factors such as total building energy consumption, lighting, and indoor thermal comfort, etc. 3.