Advancement in multi-objective optimization for building energy use based on genetic algorithms

doi:10.3969/j.issn.2097-0706.2024.09.009

Abstract

Abstract:

Currently， fossil energy consumption accounts for a high proportion of energy use in buildings in China， which is not conducive to achieving the "dual-carbon" goals. This paper discusses the current status of green building energy systems and highlights the potential of technologies such as renewable energy integration， waste heat recovery， and energy storage in improving building energy efficiency and reducing carbon emissions. Researchers often focus on optimizing building energy consumption， indoor comfort， and construction costs by building physical models using simulation software and selecting appropriate algorithms for multi-objective optimization. The paper explores the advantages and disadvantages of existing technologies and algorithms in multi-objective optimization of building energy use， emphasizing that genetic algorithms can achieve good optimization results in terms of building energy consumption， indoor comfort， and construction costs， thus providing strong support for building design and renovation decisions. In the future， there is a need to develop new multi-objective optimization algorithms and establish a comprehensive big data platform for intelligent energy management to expand all-scenario applications and achieve the perfect integration of building energy use and intelligent management.

Key words: "dual-carbon" goals, green buildings, building energy consumption, renewable energy, waste heat recovery, carbon emissions, energy storage, multi-objective optimization, genetic algorithm, integrated energy

CLC Number:

TK01：TU201.5

FAN Yanbo, XIONG Yaxuan, LI Xiang, TIAN Xi, YANG Yang. Advancement in multi-objective optimization for building energy use based on genetic algorithms[J]. Integrated Intelligent Energy, 2024, 46(9): 69-85.

Figures/Tables 15

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Table 1

Fig.8

Table 2

Fig.9

Table 3

Fig.10

Fig.11

Table 4

References 113

[1]	DUAN H B, ZHOU S, JIANG K J, et al. Assessing China's efforts to pursue the 1.5 ℃ warming limit[J]. Science, 2021, 372(6540): 378-385.
[2]	LIU Z J, LIU Y W, HE B J, et al. Application and suitability analysis of the key technologies in nearly zero energy buildings in China[J]. Renewable and Sustainable Energy Reviews, 2019, 101: 329-345.
[3]	井洋, 谢晓云, 江亿. 利用数据中心余热供热的系统设计与分析[J]. 暖通空调, 2024, 54(7): 152-158.
	JING Yang, XIE Xiaoyun, JIANG Yi. Design and analysis of district heating systems using waste heat from data centers[J]. Heating Ventilating & Air Conditioning, 2024, 54(7): 152-158.
[4]	CANNAVALE A, IERARDI L, HÖRANTNER M, et al. Improving energy and visual performance in offices using building integrated perovskite-based solar cells: A case study in Southern Italy[J]. Applied Energy, 2017, 205: 834-846.
[5]	MACHAIRAS V, TSANGRASSOULIS A, AXARLI K. Algorithms for optimization of building design: A review[J]. Renewable and Sustainable Energy Reviews, 2014, 31: 101-112.
[6]	MAGNIER L, HAGHIGHAT F. Multiobjective optimization of building design using TRNSYS simulations,genetic algorithm, and artificial neural network[J]. Building and Environment, 2010, 45(3): 739-746.
[7]	GOU S Q, NIK V M, SCARTEZZINI J L, et al. Passive design optimization of newly-built residential buildings in Shanghai for improving indoor thermal comfort while reducing building energy demand[J]. Energy and Buildings, 2018, 169: 484-506.
[8]	CHIANDUSSI G, CODEGONE M, FERRERO S, et al. Comparison of multi-objective optimization methodologies for engineering applications[J]. Computers & Mathematics with Applications, 2012, 63(5): 912-942.
[9]	何蓝玲. 基于改进NSGA-Ⅱ算法的绿色施工项目多目标优化研究[D]. 贵阳: 贵州大学, 2022.
	HE Lanling. Research on multi-objective optimization of green construction project based on improved NSGA-Ⅱ algorithm[D]. Guiyang: Guizhou University, 2022.
[10]	张浩杰. 被动式超低能耗建筑外围护结构保温层厚度多目标优化研究[D]. 湘潭: 湖南科技大学, 2022.
	ZHANG Haojie. Multi objective optimization research on insulation layer thickness of passive ultra-low energy building envelope structure[D]. Xiangtan: Hunan University of Science and Technology, 2022.
[11]	ZHANG L, DONG J K, DENG S M, et al. An experimental study on the starting characteristics of an improved radiant-convective air source heat pump system[J]. Energy and Buildings, 2020, 226:110384.
[12]	SARTORI I, NAPOLITANO A, VOSS K, et al. Net zero energy buildings: A consistent definition framework[J]. Energy and Buildings, 2012, 48:220-232.
[13]	NIVEDITHA N, RAJAN SINGARAVEL M M, et al. Optimal sizing of hybrid PV-wind-battery storage system for net zero energy buildings to reduce grid burden[J]. Applied Energy, 2022, 324:119713.
[14]	AMARAL A R, RODRIGUES E, RODRIGUES GASPAR A, et al. Review on performance aspects of nearly zero-energy districts[J]. Sustainable Cities and Society, 2018, 43:406-420.
[15]	ESBENSE T V, KORSGAAR V. Dimensioning of the solar heating system in the zero energy house in Denmark[J]. Solar Energy, 1976, 19:195-119.
[16]	BAÑOS R, MANZANO-AGUGLIARO F, MONTOYA F, et al. Optimization methods applied to renewable and sustainable energy: A review[J]. Renewable and Sustainable Energy Reviews, 2010, 15(4):1753-1766.
[17]	YE L, CHENG Z J, WANG Q Q, et al. Overview on green building label in China[J]. Renewable Energy, 2013, 53:220-229.
[18]	ØSTERGAARD P A, DUIC N, NOOROLLAHI Y, et al. Recent advances in renewable energy technology for the energy transition[J]. Renewable Energy, 2021, 179:877-884.
[19]	STADLER M, GROISSBÖCK M, CARDOSO G, et al. Optimizing distributed energy resources and building retrofits with the strategic DER-CAM[J]. Applied Energy, 2014, 132:557-567.
[20]	WEI M, LEE S H, HONG T Z, et al. Approaches to cost-effective near-net zero energy new homes with time-of-use value of energy and battery storage[J]. Advances in Applied Energy, 2021, 2:100018.
[21]	LU Y H, ALGHASSAB M, ALVAREZ-ALVARADO M S, et al. Optimal distribution of renewable energy systems considering aging and long-term weather effect in net-zero energy building design[J]. Sustainability, 2020, 12: 5570.
[22]	YAN R J, LU Z R, WANG J J, et al. Stochastic multi-scenario optimization for a hybrid combined cooling, heating and power system considering multi-criteria[J]. Energy Conversion and Management, 2021, 233:113911.
[23]	WU H F, ZHANG B W, QU W J, et al. Integration of a thermochemical energy system driven by solar energy and biomass for natural gas and power production[J]. Science China Technological Sciences, 2022, 65:1383-1395.
[24]	HERNÁNDEZ-CALLEJO L, GALLARDO-SAAVEDRA S, ALONSO-GÓMEZ V, et al. A review of photovoltaic systems: Design, operation and maintenance[J]. Solar Energy, 2019, 188:426-440.
[25]	TRAIRAT P, BANJERDPONGCHAI D. Multi-objective optimal operation of building energy management systems with thermal and battery energy storage in the presence of load uncertainty[J]. Sustainability, 2022, 14:12717.
[26]	LIN X M, LING Z Y, FANG X M, et al. Flexibility and shape memory of phase change material capable of rapid electric heating function for wearable thermotherapy[J]. Applied Energy, 2022, 327:120141.
[27]	PIMM A J, PALCZEWSKI J, MORRIS R, et al. Community energy storage: A case study in the UK using a linear programming method[J]. Energy Conversion and Management, 2020, 205:112388.
[28]	ZHENG X Y, WU G C, QIU Y W, et al. A MINLP multi-objective optimization model for operational planning of a case study CCHP system in urban China[J]. Applied Energy, 2018, 210:1126-1140.
[29]	BAHMANI R, KARIMI H, JADID S. Cooperative energy management of multi-energy hub systems considering demand response programs and ice storage[J]. International Journal of Electrical Power & Energy Systems, 2021, 130:106904.
[30]	ALAJMI A, ABOU-ZIYAN H, GHONEIM A. Achieving annual and monthly net-zero energy of existing building in hot climate[J]. Applied Energy, 2016, 165:511-521.
[31]	MAÍRA A, ARTUR K, MATEUS B, et al. Achieving mid-rise NZEB offices in Brazilian urban centres: A control strategy with desk fans and extension of set point temperature[J]. Energy and Buildings, 2022, 259:111911.
[32]	MA T, LI M, KAZEMIAN A. Photovoltaic thermal module and solar thermal collector connected in series to produce electricity and high-grade heat simultaneously[J]. Applied Energy, 2020, 261:114380.
[33]	QIU Y, ZHOU S Y, WANG J H, et al. Feasibility analysis of utilising underground hydrogen storage facilities in integrated energy system: Case studies in China[J]. Applied Energy, 2020, 269:115140.
[34]	LI G Q, SHITTU S, ZHOU K, et al. Preliminary experiment on a novel photovoltaic-thermoelectric system in summer[J]. Energy, 2019, 188:116041.
[35]	YANG W J, GUO J, VARTOSH A. Optimal economic-emission planning of multi-energy systems integrated electric vehicles with modified group search optimization[J]. Applied Energy, 2022, 311:118634.
[36]	GUO J C, LIU Z J, WU X, et al. Two-layer co-optimization method for a distributed energy system combining multiple energy storages[J]. Applied Energy, 2022, 322:119486.
[37]	ZHU G Y, CHOW T T. Design optimization and two-stage control strategy on combined cooling, heating and power system[J]. Energy Conversion and Management, 2019, 199:111869.
[38]	WANG C S, LV C X, LI P, et al. Modeling and optimal operation of community integrated energy systems: A case study from China[J]. Applied Energy, 2018, 230:1242-1254.
[39]	SOMMA D M, YAN B, BIANCO N, et al. Design optimization of a distributed energy system through cost and exergy assessments[J]. Energy Procedia, 2017, 105:2451-2459.
[40]	LIU Z J, FAN G Y, SUN D K, et al. A novel distributed energy system combining hybrid energy storage and a multi-objective optimization method for nearly zero-energy communities and buildings[J]. Energy, 2022, 239:122577.
[41]	李云, 周世杰, 胡哲千, 等. 基于NSGA-Ⅱ-WPA的综合能源系统多目标优化调度[J]. 综合智慧能源, 2024, 46(4):1-9. doi: 10.3969/j.issn.2097-0706.2024.04.001
	LI Yun, ZHOU Shijie, HU Zheqian, et al. Optimal scheduling of integrated energy systems based on NSGA-Ⅱ-WPA[J]. Integrated Intelligent Energy, 2024, 46(4):1-9. doi: 10.3969/j.issn.2097-0706.2024.04.001
[42]	朱俊铭, 柏晶晶. 基于改进遗传算法的园区综合能源系统规划研究[J]. 电力需求侧管理, 2024, 26(3): 76-81.
	ZHU Junming, BAI Jingjing. Research on multi-objective optimal allocation of park integrated energy system based on improved genetic algorithm[J]. Power Demand Side Management, 2024, 26(3): 76-81.
[43]	CIARDIELLO A, ROSSO F, DELL'OLMO J, et al. Multi-objective approach to the optimization of shape and envelope in building energy design[J]. Applied Energy, 2020, 280: 115984.
[44]	HAMDY M, ALA H S, KAI S R. Applying a multi-objective optimization approach for Design of low-emission cost-effective dwellings[J]. Building and Environment, 2011, 46(1): 109-123.
[45]	EVINS R. A review of computational optimisation methods applied to sustainable building design[J]. Renewable and Sustainable Energy Reviews, 2013, 22: 230-245.
[46]	VACCARI M, MANCUSO G M, RICCARDI J, et al. A sequential linear programming algorithm for economic optimization of hybrid renewable energy systems[J]. Journal of Process Control, 2019, 74: 189-201.
[47]	MERLET Y, ROUCHIER S, JAY A, et al. Integration of phasing on multi-objective optimization of building stock energy retrofit[J]. Energy and Buildings, 2022, 257: 111776.
[48]	ATTIA S, HAMDY M, O'BRIEN W, et al. Assessing gaps and needs for integrating building performance optimization tools in net zero energy buildings design[J]. Energy and Buildings, 2013, 60: 110-124.
[49]	ZHAO J, DU Y H. Multi-objective optimization design for windows and shading configuration considering energy consumption and thermal comfort: A case study for office building in different climatic regions of China[J]. Solar Energy, 2020, 206: 997-1017.
[50]	LI K J, PAN L, XUE W P, et al. Multi-objective optimization for energy performance improvement of residential buildings: A comparative study[J]. Energies, 2017, 10(2):245.
[51]	YU X J, GEN M. Introduction to evolutionary algorithms[M]. London: Springer, 2010.
[52]	FAN Y L, XIA X H. Energy-efficiency building retrofit planning for green building compliance[J]. Building and Environment, 2018, 136: 312-321.
[53]	DI SOMMA M, YAN B, BIANCO N, et al. Operation optimization of a distributed energy system considering energy costs and exergy efficiency[J]. Energy Conversion and Management, 2015, 103: 739-751.
[54]	HORSLEY A, FRANCE C, QUATERMASS B. Delivering energy efficient buildings: A design procedure to demonstrate environmental and economic benefits[J]. Construction Management and Economics, 2003, 21(4): 345-356.
[55]	JUAN Y K, PERNG Y H, CASTRO-LACOUTURE D, et al. Housing refurbishment contractors selection based on a hybrid fuzzy-QFD approach[J]. Automation in Construction, 2009, 18(2): 139-144.
[56]	RADFORD A D, GERO J S. On optimization in computer aided architectural design[J]. Building and Environment, 1980, 15(2): 73-80.
[57]	RADFORD A D, GERO J S. Tradeoff diagrams for the integrated design of the physical environment in buildings[J]. Building and Environment, 1980, 15(1): 3-15.
[58]	ASADI E, SILVA M GDA, ANTUNES C H, et al. Multi-objective optimization for building retrofit: A model using genetic algorithm and artificial neural network and an application[J]. Energy and Buildings, 2014, 81: 444-456.
[59]	COSTA-CARRAPIÇO I, RASLAN R, GONZÁLEZ J N. A systematic review of genetic algorithm-based multi-objective optimisation for building retrofitting strategies towards energy efficiency[J]. Energy and Buildings, 2020, 210: 109690.
[60]	CARRERAS J, BOER D, GUILLÉN-GOSÁLBEZ G, et al. Multi-objective optimization of thermal modelled cubicles considering the total cost and life cycle environmental impact[J]. Energy and Buildings, 2015, 88: 335-346.
[61]	MOHSIN M, YIN H B, ZHANG L Y, et al. An application of multiple-criteria decision analysis for risk prioritization and management: A case study of the fisheries sector in Pakistan[J]. Sustainability, 2022, 14(14): 8831.
[62]	MARLER R T, ARORA J S. The weighted sum method for multi-objective optimization: New insights[J]. Structural and Multidisciplinary Optimization, 2010, 41(6): 853-862.
[63]	COELLO A C. An updated survey of GA-based multiobjective optimization techniques[J]. ACM Computing Surveys (CSUR), 2000, 32(2):109-143.
[64]	FAN Y L, XIA X H. A multi-objective optimization model for energy-efficiency building envelope retrofitting plan with rooftop PV system installation and maintenance[J]. Applied Energy, 2017, 189: 327-335.
[65]	SHAO Y M, GEYER P, LANG W. Integrating requirement analysis and multi-objective optimization for office building energy retrofit strategies[J]. Energy and Buildings, 2014, 82: 356-368.
[66]	GUO L, LIU W J, CAI J J, et al. A two-stage optimal planning and design method for combined cooling, heat and power microgrid system[J]. Energy Conversion and Management, 2013, 74: 433-445.
[67]	XU C B, KE Y M, LI Y B, et al. Data-driven configuration optimization of an off-grid wind/PV/hydrogen system based on modified NSGA-Ⅱ and CRITIC-TOPSIS[J]. Energy Conversion and Management, 2020, 215: 112892.
[68]	MATSUMOTO T, MASUYAMA N, NOJIMA Y, et al. Performance comparison of multiobjective evolutionary algorithms on problems with partially different properties from popular test suites[C]// Proceedings of 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2018: 769-774.
[69]	CHEN X, CAO B Y, POURAMINI S. Energy cost and consumption reduction of an office building by chaotic satin bowerbird optimization algorithm with model predictive control and artificial neural network: A case study[J]. Energy, 2023, 270: 126874.
[70]	施文鹏, 孙岑花, 李佳俊, 等. 基于人工神经网络智能算法的9310钢本构模型优化[J]. 精密成形工程, 2024, 16(3): 171-180.
	SHI Wenpeng, SUN Cenhua, LI Jiajun, et al. 9310 steel constitutive model optimization based on artificial neural network intelligent algorithm[J]. Journal of Netshape Forming Engineering, 2024, 16(3): 171-180.
[71]	KHALAJ G, NAZARI A, YOOZBASHIZADEH H, et al. ANN model to predict the effects of composition and heat treatment parameters on transformation start temperature of microalloyed steels[J]. Neural Computing and Applications, 2014, 24(2): 301-308.
[72]	KOU F C, GONG Q P, ZOU Y, et al. Solar application potential and thermal property optimization of a novel zero-carbon heating building[J]. Energy and Buildings, 2023, 279: 112688.
[73]	张鲁平. 粒子群优化算法的理论分析及改进研究[D]. 牡丹江: 牡丹江师范学院, 2024.
	ZHANG Luping. Theoretical analysis and improvement of particle swarm optimization algorithm[D]. Mudanjiang: Mudanjiang Normal University, 2024.
[74]	CARLUCCI S, PAGLIANO L, ZANGHERI P. Optimization by discomfort minimization for designing a comfortable net zero energy building in the Mediterranean climate[J]. Advanced Materials Research, 2013, 689: 44-48.
[75]	ELBELTAGI E, HEGAZY T, GRIERSON D. Comparison among five evolutionary-based optimization algorithms[J]. Advanced Engineering Informatics, 2005, 19(1): 43-53.
[76]	MOKHTARI Y, REKIOUA D. High performance of maximum power point tracking using ant colony algorithm in wind turbine[J]. Renewable Energy, 2018, 126: 1055-1063.
[77]	赵丹. 基于蚁群算法的建筑工程项目多目标优化研究[D]. 邯郸: 河北工程大学, 2016.
	ZHAO Dan. Research on multi-objective optimization of construction projects based on ant colony algorithm[D]. Handan: Hebei University of Engineering, 2016.
[78]	刘香香, 孙凤. 基于蚁群算法的装配式建筑施工工序多目标优化模型[J]. 土木工程与管理学报, 2021, 38(3): 113-118.
	LIU Xiangxiang, SUN Feng. Multi objective optimization model of installation process of prefabricated buildings based on ant colony algorithm[J]. Journal of Civil Engineering and Management, 2021, 38(3): 113-118.
[79]	LI L, FU Y F, FUNG J C H, et al. Development of a back-propagation neural network and adaptive grey wolf optimizer algorithm for thermal comfort and energy consumption prediction and optimization[J]. Energy and Buildings, 2021, 253: 111439.
[80]	周波. 基于灰狼优化算法的楼宇负荷多目标优化调度研究[D]. 湘潭: 湘潭大学, 2020.
	ZHOU Bo. Research on multi-objective optimal scheduling of building load based on grey wolf optimization algorithm[D]. Xiangtan: Xiangtan University, 2020.
[81]	张良, 郑丽冬, 冷祥彪, 等. 基于灰狼算法的风-光-抽水蓄能联合系统多目标优化策略研究[J/OL]. 上海交通大学学报, 2023:1-25[2023-05-29](2024-05-01). https://doi.org/10.16183/j.cnki.jsjtu.2023.049.
	ZHANG Liang, ZHENG Lidong, LENG Xiangbiao, et al. Research on multi-objective optimization strategy of wind-photovoltaic-pumped storage combined system based on gray wolf algorithm[J/OL]. Journal of Shanghai Jiaotong University, 2023:1-25[2023-05-29](2024-05-01).https://doi.org/10.16183/j.cnki.jsjtu.2023.049.
[82]	BROWNLEE A E I, WRIGHT J A, MOURSHED M M. A multi-objective window optimisation problem[C]// 13th Annual Genetic and Evolutionary Computation Conference, GECCO 2011:2001910.
[83]	PENNA P L, PRADA A, CAPPELLETTI F, et al. Multi-objective optimization for existing buildings retrofitting under government subsidization[J]. Science and Technology for the Built Environment, 2015, 21(6):847-861.
[84]	GARCÍA KERDAN I, RASLAN R, RUYSSEVELT P, et al. A comparison of an energy/economic-based against an exergoeconomic-based multi-objective optimisation for low carbon building energy design[J]. Energy, 2017, 128: 244-263.
[85]	KHEIRI F. A review on optimization methods applied in energy-efficient building geometry and envelope design[J]. Renewable and Sustainable Energy Reviews, 2018, 92: 897-920.
[86]	VARMA P, BHATTACHARJEE B. Evaluating performance of simulated annealing and genetic based approach in building envelope optimization[C]// Conference of International Building Performance Simulation Association. IBPSA, 2015:2088-2092.
[87]	HAMDY M, NGUYEN A T, HENSEN J L M. A performance comparison of multi-objective optimization algorithms for solving nearly-zero-energy-building design problems[J]. Energy and Buildings, 2016, 121: 57-71.
[88]	JUNGHANS L, DARDE N. Hybrid single objective genetic algorithm coupled with the simulated annealing optimization method for building optimization[J]. Energy and Buildings, 2015, 86: 651-662.
[89]	WETTER M, WRIGHT J. A comparison of deterministic and probabilistic optimization algorithms for nonsmooth simulation-based optimization[J]. Building and Environment, 2004, 39(8): 989-999.
[90]	KONAK A, COIT D W, SMITH A E. Multi-objective optimization using genetic algorithms: A tutorial[J]. Reliability Engineering & System Safety, 2006, 91(9): 992-1007.
[91]	BICHIOU Y, KRARTI M. Optimization of envelope and HVAC systems selection for residential buildings[J]. Energy and Buildings, 2011, 43(12): 3373-3382.
[92]	ASCIONE F, BIANCO N, DE STASIO C, et al. CASA, cost-optimal analysis by multi-objective optimisation and artificial neural networks: A new framework for the robust assessment of cost-optimal energy retrofit, feasible for any building[J]. Energy and Buildings, 2017, 146: 200-219.
[93]	FADAEE M, RADZI M A M. Multi-objective optimization of a stand-alone hybrid renewable energy system by using evolutionary algorithms: A review[J]. Renewable and Sustainable Energy Reviews, 2012, 16(5): 3364-3369.
[94]	FERREIRA J, DUARTE PINHEIRO M, DE BRITO J. Refurbishment decision support tools: A review from a Portuguese user's perspective[J]. Construction and Building Materials, 2013, 49: 425-447.
[95]	NGUYEN A T, REITER S, RIGO P. A review on simulation-based optimization methods applied to building performance analysis[J]. Applied Energy, 2014, 113: 1043-1058.
[96]	SHI X, TIAN Z C, CHEN W Q, et al. A review on building energy efficient design optimization from the perspective of architects[J]. Renewable and Sustainable Energy Reviews, 2016, 65: 872-884.
[97]	ASADI E, SILVA M GDA, ANTUNES C H, et al. Multi-objective optimization for building retrofit strategies: A model and an application[J]. Energy and Buildings, 2012, 44: 81-87.
[98]	ASADI E, SILVA M GDA, ANTUNES C H, et al. State of the art on retrofit strategies selection using multi-objective optimization and genetic algorithms[M]// Nearly Zero Energy Building Refurbishment. London: Springer London, 2013: 279-297.
[99]	JEAN B. Climatic design of vernacular housing in different provinces of China[J]. Journal of Environmental Management, 2008, 87(2): 287-299. pmid: 17764822
[100]	O'NEILL M. A field guide to genetic programming[J]. Genet Program Evolvable Mach, 2009, 10(2):229-230.
[101]	DEB K. Multi-objective optimization using evolutionary algorithms[M]. Chichester: John Wiley & Sons, 2001.
[102]	DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-Ⅱ[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197.
[103]	MAKAREMI Y, HAGHIGHI A, GHAFOURI H R. Optimization of pump scheduling program in water supply systems using a self-adaptive NSGA-Ⅱ:A review of theory to real application[J]. Water Resources Management, 2017, 31(4): 1283-1304.
[104]	CHAUBE A, BENYOUCEF L, TIWARI M K. An adapted NSGA-2 algorithm based dynamic process plan generation for a reconfigurable manufacturing system[J]. Journal of Intelligent Manufacturing, 2012, 23(4): 1141-1155.
[105]	LONGO S, MONTANA F, RIVA SANSEVERINO E. A review on optimization and cost-optimal methodologies in low-energy buildings design and environmental considerations[J]. Sustainable Cities and Society, 2019, 45: 87-104.
[106]	JONES D F, MIRRAZAVI S K, TAMIZ M. Multi-objective meta-heuristics: An overview of the current state-of-the-art[J]. European Journal of Operational Research, 2002, 137(1): 1-9.
[107]	GARCÍA KERDAN I, RASLAN R, RUYSSEVELT P, et al. ExRET-Opt: An automated exergy/exergoeconomic simulation framework for building energy retrofit analysis and design optimisation[J]. Applied Energy, 2017, 192: 33-58.
[108]	DELGARM N, SAJADI B, DELGARM S, et al. A novel approach for the simulation-based optimization of the buildings energy consumption using NSGA-Ⅱ: Case study in Iran[J]. Energy and Buildings, 2016, 127: 552-560.
[109]	MANZAN M. Genetic optimization of external fixed shading devices[J]. Energy and Buildings, 2014, 72: 431-440.
[110]	BRE F, FACHINOTTI V D. A computational multi-objective optimization method to improve energy efficiency and thermal comfort in dwellings[J]. Energy and Buildings, 2017, 154: 283-294.
[111]	D'AGOSTINO D, D'AGOSTINO P, MINELLI F, et al. Proposal of a new automated workflow for the computational performance-driven design optimization of building energy need and construction cost[J]. Energy and Buildings, 2021, 239: 110857.
[112]	ASCIONE F, BIANCO N, IOVANE T, et al. A real industrial building: Modeling, calibration and Pareto optimization of energy retrofit[J]. Journal of Building Engineering, 2020, 29: 101186.
[113]	SON H, KIM C. Evolutionary many-objective optimization for retrofit planning in public buildings: A comparative study[J]. Journal of Cleaner Production, 2018, 190: 403-410.

作者	优化方法	对比结论
Hamdy^[87]	pNSGA-Ⅱ，MOPSO，PR-GA，ENSES，evMOGA，SpMODE-Ⅱ，MODA	在大多数情况下，所获得解质量的提高与代数的增加有直接关系； PR-GA算法的搜索覆盖了大范围的解空间，包含了具有良好多样性的近优解。PNSGA-Ⅱ，evMOGA和spMODE-Ⅱ在PR-GA后表现出相似的特征；大多数案例中，ENSES，MOPSO和MODA取得了最好的优化结果
Junghans，Dande^[88]	GA，SA，Hybrid，GA-SA	混合GA-SA产生了可靠的结果； GA的种群越大，结果越好；虽然精英主义保证了上一代的最佳解决方案，但无法一直得到更好的平均目标函数
Wetter，Wright^[89]	HJ，GA	GA在测试的3个城市中有2个城市的结果比HJ好，可能是HJ算法由于问题的不连续性导致计算失败，或者陷入局部最优；较大的不连续性可能降低了HJ算法的收敛速度

算法	评估次数
算法	200	600	1 000	1 400	1 800
pNSGA-Ⅱ	18.6	16.9	0	0	1.8
MOSPO	20.9	6.8	6.7	3.6	10.3
PR-GA	7.0	27.1	66.7	73.2	50.0
evMOGA	20.9	22.0	5.0	5.4	6.9
ENSES	2.3	0	0	0	0
spMODE-Ⅱ	25.6	25.4	21.6	16.1	31.0
MODA	4.7	1.8	0	1.7	0

城市	目标函数	基线值/GJ	优化值/GJ	差异度/%
大不里士	年制冷耗电量	1.08	0.31	-71.3
	年照明耗电量	1.90	1.96	+3.2
	年总耗电量	2.98	2.27	-23.8
德黑兰	年制冷耗电量	2.66	0.66	-75.2
	年照明耗电量	1.89	1.97	+4.2
	年总耗电量	4.55	2.63	-42.2
科曼	年制冷耗电量	1.74	0.41	-76.4
	年照明耗电量	1.91	1.93	+1.0
	年总耗电量	3.65	2.34	-35.9
阿巴斯港	年制冷耗电量	3.35	1.48	-55.8
	年照明耗电量	1.89	1.98	+4.8
	年总耗电量	5.24	3.46	-34.0

项目	光伏占比/%
项目	0	40	60
年一次能源消耗量/ [(kW·h)·m^-2]	215.7	61.6	40.1
平均预计不满意百分比/%	13.0	12.4	12.9
总成本/欧元	74 700	68 500	41 300
净现值/欧元		6 040	33 000
投资回收期/a		17.6	12.4
年均CO₂当量排放量/t	25.00	7.15	4.65