Multi-objective optimization design of a complex building based on an artificial neural network and performance evaluation of algorithms |
| |
| Affiliation: | 1. School of Architecture, Southeast University, Nanjing, China;2. Key Laboratory of Urban and Architectural Heritage Conservation, Ministry of Education, China;1. School of Design, University of Pennsylvania, 207 Meyerson Hall, 210 South 34th Street, Philadelphia, PA 19104-6311, USA;2. Department of Architecture, College of Architecture, Texas A&M University, 3137 TAMU, College Station, TX 77843, USA;1. College of Architecture, Construction and Planning, University of Texas at San Antonio, United States;2. Young Researchers Club, Central Tehran Branch, Islamic Azad University, P.O. Box 13185-768, Tehran, Iran;3. Petrochemical Research and Technology Company, National Petrochemical Company, P.O. Box 14358-84711, Tehran, Iran;1. School of Architecture, Southeast University, PR China;2. Key Laboratory of Urban and Architectural Heritage Conservation, Ministry of Education, PR China;1. Department of the Built Environment, Building Physics and Services, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands;2. Department of Mechanical Power Engineering, Helwan University, P.O. Box 11718, Cairo, Egypt;3. Department of Energy Technology, Aalto University School of Engineering, P.O. Box 14400, FI-00076 Aalto, Finland;4. Faculty of Architecture, The University of Danang—University of Science and Technology, 54 Nguyen Luong Bang, Danang, Viet Nam;1. MIT-Portugal Program, Department of Mechanical Engineering, University of Coimbra, Coimbra, Portugal;2. ADAI–LAETA, Department of Mechanical Engineering, University of Coimbra, Coimbra, Portugal;3. INESC Coimbra, 3000 Coimbra, Portugal;4. Department of Electrical Engineering and Computers, Polo II, University of Coimbra, 3030 Coimbra, Portugal;5. Faculty of Economics, University of Coimbra, Av. Dias da Silva, Coimbra, Portugal;6. Department of Architecture, Massachusetts Institute of Technology, Cambridge, MA 02139, USA;1. Woowon Mechanical & Environmental Engineers, Sillim-Dong, Gwanak-Gu, Seoul, 151-904, Republic of Korea;2. Division of Architectural Engineering, College of Engineering, Hanyang University, Seoul 133-791, Republic of Korea |
| |
| Abstract: | While optimization studies focusing on real-world buildings are somewhat limited, many building optimization studies to date have used simple hypothetical buildings for the following three reasons: (1) the shape and form of real buildings are complex and difficult to mathematically describe; (2) computer models built based on real buildings are computationally expensive, which makes the optimization process time-consuming and impractical and (3) although algorithm performance is crucial for achieving effective building performance optimization (BPO), there is a lack of agreement regarding the proper selection of optimization algorithms and algorithm control parameters. This study applied BPO to the design of a newly built complex building. A number of design variables, including the shape of the building’s eaves, were optimized to improve building energy efficiency and indoor thermal comfort. Instead of using a detailed simulation model, a surrogate model developed by an artificial neural network (ANN) was used to reduce the computing time. In this study, the performance of four multi-objective algorithms was evaluated by using the proposed performance evaluation criteria to select the best algorithm and parameter values for population size and number of generations. The performance evaluation results of the algorithms implied that NSGA-II (with a population size and number of generations of 40 and 45, respectively) performed the best in the case study. The final optimal solution significantly improves building performance, demonstrating the success of the BPO technique in solving complex building design problems. In addition, the findings on the performance evaluation of the algorithms provide guidance for users regarding the selection of suitable algorithms and parameter settings based on the most important performance criteria. |
| |
| Keywords: | Building design optimization Artificial neural network Multi-objective optimization algorithms Performance evaluation of algorithms Real-world building design |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
