بررسی کارایی روشهای بهینه سازی تکاملی در دستیابی به اهداف معماری و ساخت
محورهای موضوعی : معماری
1 - دانشجوی کارشناسی ارشد دانشکده معماری و شهرسازی، دانشگاه فردوسی مشهد، مشهد، ایران
2 - استادیار دانشکده معماری و شهرسازی، دانشگاه فردوسی مشهد، مشهد، ایران.
کلید واژه: روشهای فرااکتشافی, بهینه سازی, فرایند طراحی معماری, الگوریتم های تکاملی,
چکیده مقاله :
با افزایش محبوبیت روش های بهینه سازی در علوم مختلف، معماران نیز با اهداف گوناگون به استفاده از این روش ها در طراحیو اجرای ساختمان پرداخت هاند. نحوه ی کارکرد و ویژگی های ه رکدام، با توجه به جدید بودن آ نها در معماری، ناشناخته است.در این تحقیق، ضمن تدوین مبانی روش های بهینه سازی تکاملی، با مرور 77 مطالعه پیشین که در حوزه ی ساختمان، ازالگوریتم های بهینه سازی استفاده کرده اند؛ به بررسی میزان کارایی رو شها در دستیابی به اهداف معماری و ساخت، ب ه روشتحلیل محتوای متن اقدام م یگردد. الگوریتم های بهینه سازی با شش هدف مختلف در معماری پیاده سازی شد هاند؛ از این اهداف،بیشترین کاربرد مربوط به بهینه سازی نظام فضایی در کاربری مسکونی و بهینه سازی انرژی در ساختمان های اداری است. دربررس ی انجام شده، الگوریتم ژنتیک پرکاربردترین الگوریتم تکاملی و بهینه سازی انبوه ذرات، رایج ترین روش در تحقیقات مبتنیبر هوش جمعی است. با توجه به برخورد عمدتاً نظری پژوهشگران با این موضوع، طراحان نیازمند تعاملات بین رشته ای بیش تر باسایر محققین ب هویژه متخصصین کامپیوتر جهت پیاده سازی و عملیاتی شدن کاربرد الگوریتم ها می باشند.
The increasing popularity of the optimization approach in different sciences has led architects to use them tomachieve various objectives in designing and cons tructing buildings. However, the functions, advantages, and limitations for each of these optimization s trategies are scarcely known, due to their newness in architecture and cons truction fields Optimization algorithms are classified into three categories: determinis tic, heuris tic, and meta-heuris tic algorithms. Meta-heuris tic algorithms, are more efficient and categorized into three main groups: evolutionary computing, swarm intelligence, and physics-related algorithms. Mos t of the s tudies conducted on optimization algorithms, in this field, are on the application of one of the optimization algorithms in the design of a particular project. Limited research has been done in coordination with the subject of this s tudy, inves tigating the application of these algorithms in a specific field. After reviewing the his tory and literature of the subject, to discuss how optimization methods are used in architecture, 77 related articles and theses that used optimization methods have been reviewed through scholar works published since 1996 (the firs t publications in this field) up to now. Selected research was analyzed using the textual content analysis method to determine "the efficiency ofevolutionary optimization methods in achieving architectural and cons truction objectives" as the main research question; there were also several sub-ques tions on the way to answer the main ques tion: Which architectural objectives are mos t achievable by using optimization algorithms? Which types of optimization algorithms are appropriate for architectural objectives? Which building functions have the mos t potential for using optimization methods? Which researchers conduct and support the research of evolutionary algorithms in building issues? Optimization algorithms have been undertaken to solve design problems for six different objectives: mass design and urban access, cons truction and cos t management, building’s s tructural design, energy issues, building form generation and space planning. Various design variables have been defined to search for optimal response to each of the objectives. Among these objectives, the highes t application of optimization algorithms is related to spatial planning optimization in residential buildings and energy optimization in official ones. The analysis of the publications demons trates that various methods of meta-heuris tic optimization have been used over time to solve architectural problems. Genetic Algorithm is the mos t widely used one in architectural optimization, and particle swarm optimization is the mos t common method in swarm intelligence based research. The review of s tudies indicates the predominantly theoretical attention of architectural scholars to this issue. Given the dis tance between the research and the implementation phase, architects should work more closely with researchers in other fields, especially those in computer science, to approach the implementation s tage. However, the development of each of these areas requires the improvement of previous methods and research into how other algorithms, such as swarm intelligence based ones, can be used to solve design problems in architecture. The development of user-friendly software with a graphical user interface for a better grasp of the design process and results can affect architects' usage of evolutionary algorithms as a design method.
اسلیمی، هیراد؛ جمالی، علی؛و نریمان زاده، نادر. (1396). بهینه‌سازی اندازه و توپولوژی سازه‌های خرپایی فضاکار با استفاده از الگوریتم ترکیبی برنامه‌ریزی ژنتیکی و نلدر-مید. مجله مهندسی مکانیک مدرس،6،32-40.
امین‌زاده گوهرریزی، بهرام؛ توحیدی راد، سعید؛و اسدی، روشنک. (1395). به‌کارگیری الگوریتم NSGA -II برای حل مسائل مکان‌یابی چندهدفه. فصلنامه مطالعات شهری،19،15-26.
جعفری، فاطمه. (1393). کاربرد الگوریتم ژنتیک در حل مسئله طراحی معماری؛ (نمونه: طراحی مجتمع مسکونی باران). پایان‌نامه کارشناسی ارشد، دانشگاه تربیت مدرس، تهران.
حیدری، مهدیه. (1394). بهینه‌سازی فرم ساختمان‌های اداری بر اساس الگوریتم‌های تکاملی در جهت توسعه پایدار. پایان‌نامه کارشناسی ارشد، دانشگاه هنر اسلامی تبریز، تبریز.
خبازی، زوبین. (1391). پاردایم معماری الگوریتمیک. مشهد: انتشارات کتابکده کسری.
گلابچی، محمود؛ اندجی گرمارودی، علی؛و باستانی، حسین. (1390). معماری دیجیتال: کاربرد فناوری‌های CAD/CAM/CAE در معماری. تهران: انتشارات دانشگاه تهران.
لطفخدایی، بهزاد؛و ابوالبشری،محمدحسین. (1395). بهینه‌سازی طرح سازه‌های پیوسته دوبعدی با استفاده از روش الگوریتم تبرید تدریجی کارآمد. مجله علمی پژوهشی مکانیک سازه‌ها و شاره‌ها، 6(4)، 41-48.
مردمی، کریم؛ سهیلی فرد، مهدی؛ و آقا عزیزی، مجید. (1394). همسازی سازه و معماری در راستای جانمایی بهینه تکیه‌گاه‌ها به روش الگوریتم ژنتیک (نمونه موردی: پوشانه‌های با فرم آزاد، طراحی‌شده بر اساس هندسه گره ایرانی). فصلنامه علـمی- پژوهـشی نقش‌جهان، 5(2)، 65-75.
مهدوی نژاد، محمدجواد؛ ارباب، مژگان؛و ارباب،مریم. (1398). استفاده از الگوریتم ژنتیک در بهینه‌سازی چند هدفه لوورهای خارجی در ساختمان‌های اداری. دوفصلنامه اندیشه معماری، 5، 214-235.
Asadi, S., & Geem, Z. W. (2015). Sustainable building design: A reviewon recent metaheuristic methods. Studies in computational intelligence, 585, 203-223.
Babaei, M., & Sheidaii, M. R. (2016). Desirability-based design of space structures using genetic algorithm and fuzzy logic. Civil engineering, 15(2), 231–245.
Bahrehmand, A., Batard, T., Marques, R., Evans, A., & Blat, J. (2017). Optimizing layout using spatial quality metrics and user preferences. Graphical models, 93, 25–38.
Bamdad, K., Cholette, M. E., Guan, L., & Bell, J. (2017). Ant colony algorithm for building energy optimisation problems and comparison with benchmark algorithms. Energy and buildings, 154, 404-414.
Banerjee, A., Quiroz, J. C., & Louis, S. J. (2008). A model of creative design using collaborative interactive genetic algorithm. Design computing and cognition, 8, 397-416.
Bausys, R., & Pankrašovaite, I. (2005). Optimization of architectural layout by the improved genetic algorithm. Civil engineering and management, 11(1), 13-21.
Bentley, P. J. (1998). The revolution of evolution in design: from coffee tables in Proceeding ofrecent advances in soft computing. July 2-3, (pp. 172–182). Leicester.
Bizjak, M., Žalik, B., Štumberger, G., & Lukač, N. (2018). Estimation and optimisation of buildings' thermal load using LiDAR data. Building and environment, 128, 12-21.
Blum, C., & Roli, A. (2003). Metaheuristics in combinatorial optimization: overview. ACM computing surveys, 35(3), 268–308.
Caldas, L., & Norford, L. K. (1999). A genetic algorithm tool for design optimization. ACADIA, 99.
Caldas, L., & Norford, L. K. (2002). A design optimization tool based on a genetic algorithm. Automation in construction, 11, 173–184.
Chen, K. W., Janssen, P., & Schlueter, A. (2018). Multi-objective optimisation of building form, envelope and cooling system for improved building energy performance. Automation in construction, 94, 449-457.
Chen, X., & Yang, H. (2017). A multi-stage optimization of passively designed high-rise residential buildings in multiple building operation scenarios. Applied energy, 206, 541-557.
Chiu, C.C., Cheng, Y.T., & Chang, C.W. (2012). Comparison of particle swarm optimization and genetic algorithm for the path loss reduction in an urban area. Applied science and engineering, 5(4), 371-380.
Cichock, J. M., Migalska, A., Browne, W. N., & Rodriguez, E. (2017). SILVEREYE– the implementation of Particle Swarm Optimization algorithm in a design optimization tool In Proceeding of 17th International Conference CAAD Futures. July 12-14, (pp.151–169). Singapore: Springer nature.
Coello Coello, C. (2002). Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: A survey of the state of the art. Computer methods in applied mechanics and engineering, 19(11), 1245-1287.
Conceição António, C. A., Monteiro, J. B., & Afonso, C. F. (2014). Optimal topology of urban buildings for maximization of annual solar irradiation availability using a genetic algorithm. Applied thermal engineering, 73(1), 424-437.
Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to algorithms. Cambridge: MIT press.
Cubukcuoglu, C., Chatzikonstantinou, I., Ekici, B., & Sariyildiz, S. (2016). Multi-objective optimization through differential evolution for restaurant design InIEEE congress on evolutionary computation (CEC). (pp. 2288 - 2295). July 24-29, Vancouver: IEEE.
Cuevas, E., Barocio, E., Emilio, E., & Arturo, C. (2019). Introduction to metaheuristics methods in metaheuristics algorithms in power systems (pp. 1 - 8). Basel: Springer.
Datta, S., Roy, S., & Davim, J. P. (2019). Optimization techniques: An overview InDatta, S.,& Davim, J. P. (Eds.), Optimization in Industry, (Vol.2, pp. 1 - 11). Basel: Springer.
Dawkins, R. (1986). The blind watchmaker.New York: Norton & Company.
Delgarm, N., Sajadi, B., Kowsary, F., & Delgarm, S. (2016). Multi-objective optimization of the building energy performance: A simulation-based approach by means of particle swarm optimization (PSO). Applied Energy, 170, 293-303.
Dhariwal, J., & Banerjee, R. (2017). An approach for building design optimization using design of experiments. Building Simulation, 10(3), 323–336.
Dino, I. G., & Üçoluk, G. (2017). Multiobjective design optimization of building space layout, energy and daylighting performance. Computing in Civil Engineering, 31(5), 1-18.
Dorigo, M., & Stützle, T. (2019). Ant colony optimization: overview and recent advances In M. Gendreau, & J. Y. Potvin (Eds.), Handbook of metaheuristics: Internationalseries in operations research and management science, (Vol.272, pp. 311-351). Basel: Springer.
Doulgerakis, A. (2007). Genetic programming + unfolding embryology in automated layout planning. Unpublished master’s thesis, Bartlett School of Graduate Studies, London.
Ekici, B., Cubukcuoglu, C., Turrin, M., & Sariyildiz, I. S. (2018). Performative computational architecture using swarm and evolutionary optimisation: A review. Building and environment, 147, 356–371.
Elezkurtaj, T., & Franck, G. (1999). Genetic algorithms in support of creative. Architectural computing from Turing to 2000, 645–651.
Evins, R. (2015). Multi-level optimization of building design. energy system sizing and operation. Energy, 90, 1775-1789.
Fasoulaki, E. (2007). Genetic algorithms in architecture: A necessity or a trend? In 10th generative art international conference.June 12-15, Milan.
Feldmeth, N. (1996). American church history," CH506, lecture "The attack upon orthodoxy. Fullertheologicalseminary, School of theology, California.
Finucane, E. L., Derix, C., & Coates, P. S. (2006). Evolving urban structures using computer optimisation techniques In Generative Art Conference. December 15-17, (pp.1‑26). Milan: Generative art
Flack, R. W. (2011). Evolution of architectural floor plans In European conference on the applications of evolutionary computation. April 27-29, (pp. 313-322). Torino: Applications of evolutionary computation.
Fogel, L. J., Owens, A., & Walsh, M. (1964). On the Evolution of Artificial Intelligence InFifth National Symposium on Human Factors in Electronics. 5-6May, (pp. 63–76). San Diego: IEEE.
Frazer, J. (1995). An Evolutionary Architecture. London: Architectural association.
Futrell, B. J., Ozelkan, E. C., & Brentrup, D. (2015). Bi-objective optimization of building enclosure design for thermal and lighting performance. Building and Environment, 92, 591- 602.
Futuyma, D. J. (2017). Evolution. (4thed.). Sunderland: Sinauer Associates.
Gerber, D. J., & Lin, S.H. E. (2014). Designing in complexity: Simulation, integration, and multidisciplinary design optimization for architecture. Simulation, 90(8), 936-959.
Gero, J. S., & Kazakov, V. A. (1997). Learning and re-using information in space layout planning problems using genetic engineering. Artificial Intelligence in Engineering, 12(3), 163-176.
Hall, B.K., Hallgrímsson, B., & Strickberger, M. W. (2008). Strickberger's evolution: the integration of genes, organisms and populations. (4thed.). Sudbury: Jones and Bartlett.
Hansen, J. M., Raut, S., & Swami, S. (2010). Retail shelf allocation: A comparative analysis of heuristic and meta-heuristicapproaches. Journal of retailing, 86 (1), 94–105.
Hansen, N., Arnold, D. V., & Auger, A. (2015). Evolution strategies In J. Kacprzyk, & W. Pedrycz (Eds.),Springer handbook of computational intelligence(Vol.44, pp. 871 - 898).Berlin: Springer.
Harkouss, F., Fardoun, F., & Henry, P. B. (2018). Multi-objective optimization methodology for net zero energy buildings. building Engineering, 16, 57-71.
Hassani, B., Tavakkoli, M., & Moghaddam, N. Z. (2011). Application of isogeometric analysis in structural shape optimization. Scientia Iranica, 18(8), 846-852.
Hegazy, T. (1999). Optimization of resource allocation and leveling using genetic algorithms. Journal of Construction Engineering and Management, 125(3), 167-175.
Homayouni, H. (2007). A genetic algorithm approach to space layout planning. Unpublished master’s thesis, University of Washington, Seattle.
Inoue, M., & Takagi, H. (2008). Layout Algorithm for an EC-based RoomInIEEE Conference on Soft Computing in Industrial Applications. June 25 - 27, (pp. 165–170). Muroran: IEEE.
Jackson, H. (2001). Toward a symbiotic coevolutionary approach to architecture In D. Corne, & P. C. Bentley (Ed.), Creative Evolutionary Systems (pp. 299–313). Burlington: Morgan Kaufmann.
Jagielski, R., & Gero, J. S. (1997). A genetic programming approach to the space. CAADfutures,875, 875–884.
Jo, J. H., & Gero, J. (1998). Space layout planning using an evolutionary approach. Artificial intelligence in engineering, 12(3), 149-162.
Juneja, M., & Nagar, S. (2016). Particle swarm optimization algorithm and its parameters: A review In International Conference on Control, Computing, Communication and Materials (ICCCCM). October 21-22, (pp. 1-5). Piscataway: IEEE.
Karatas, A., & El-Rayes, K. (2014). Optimal trade-offs between social quality of life and life-cycle cost in housing units. Construction Engineering and Management, 140(12), 1-9.
Khajehpour, S., & Grierson, D. E. (2003). Profitability versus safety of high-rise office buildings. Structural and Multidisciplinary Optimization, 25(4), 279–293.
Kirimtat, A., Koyunbaba, B. K., Chatzikonstantinou, I., Sariyildiz, S., & Suganthan, P. N. (2016). Multi-objective optimization for shading devices in buildings by using evolutionary algorithms InIEEE Congress on Evolutionary Computation (CEC).July 24-29, (pp. 3917 – 3924). Vancouver: IEEE.
Knecht, K., & Koenig, R. (2010). Generating floor plan layouts with k-d trees and evolutionary algorithms InGA2010 - 13th Generative Art Conference. December 15-17, (pp. 238–253). Milan: Domus Argenia Publisher.
Koma, S., Yamabe, Y., & Tani, A. (2017). Research on urban landscape design using the interactive genetic algorithm and 3D images. Visualization in Engineering, 5(1), 1-10.
Koza, J. R. (1997). Genetic programming In H. Henderson (Ed.),Encyclopedia of computer science and technology (Vol.2, pp. 441–885). New York: Infobase publishing.
Kumar, A., Dutta, K., Gupta, A., Badyal, S., & Rohan, D. (2017). Assisting an architect with alternative automated space layout designs using order crossover genetic algorithm in AutocadinInternational conference on advances in mechanical industrial, Automation and management systems (AMIAMS). February 3-5, IEEE.
Lee, D., Lim, H., Kim, T., Cho, H., & Kang, K.I. (2018). Advanced planning model of formwork layout for productivity improvement. Automation in Construction, 85, 232–240.
Li, L. (2012). The optimization of architectural shape based on genetic algorithm.Frontiers of architectural research, 1(4), 392-399.
Li, Y., Chen, Y., Zhao, M., & Zha, X. (2018). Optimization of planning layout of urban building based on improved logit and PSO algorithms, Complexity, 2018,1-11.
Machairas, V., Tsangrassoulis, A., & Axarli, K. (2014). Algorithms for optimization of building design: A review. Renewable and sustainable energy reviews, 31, 101-112.
Makris, D., Ioannis, H., Georges, M., & Dimitri, P. (2006). Multicad - MOGA: A system for conceptual style design of buildings incomputer graphics and artificial intelligence conference. May 23-24, (pp. 73-84).
Marin, P., Bignon, J.-C., & Lequay, H. (2008). A genetic algorithm for use in creative design processes. Halshs. Archives-ouvertes InAnnual conference of the association for computer aided design in architecture (ACADIA). October 31, Minneapolis: halshs.
Michalek, J. J., & Papalambros, P. Y. (2002). Interactive design optimization of architectural layouts. Engineering optimization, 34(5), 485–501.
Mukherjee, I., & Ray, P. K. (2006). A review of optimization techniques in metal cutting processes. Computers & Industrial Engineering, 50(1-2), 15–34.
Naboni, E., Malcangi, A., Zhang, Y., & Barzon, F. (2015). Defining the energy saving potential of architectural design. Energy procedia, 83, 140-146.
Nagy, D., Lau, D., Locke, J., Stoddart, J., Villaggi, L., Wang, R., Zhao, D. &Benjamin, D. (2017). Project Discover: An application of generative design for architectural space planning In SIMAUD 17: Proceedings of the symposium on simulation for architecture and urban design. May 22-24, (pp 1-8).Torento: John H. Daniels faculty of architecture.
Nisztuk, M., & Paweł, M. B. (2018). Sability of contemporary tools for the computational design of architectural objects: Review, features evaluation and reflection. Architectural computing, 16(1), 58 –84.
Özer, D. G., & Şener, S. M. (2013). User accessibility optimization using genetic algorithm: ADA. A/Z ITU Journal of the faculty of architecture, 10(2), 212-230.
Papadaki, I. N., & Chassiakos, A. P. (2016). Multi-objective construction site layout planning using genetic algorithms In M.H. Miroslaw, & J. Skibniewski (Eds.),Creative construction conference2016. June 25-28, (Vol.164, pp. 20 – 27). Amsterdam: Elsevier B.V.
Papapavlou, A., & Turner, A. (2009). Structural evolution: A genetic algorithm method to generate structurally optimal Delaunay triangulated space frames for dynamic loadsin27th ecaade Conference. September 14-15,(pp. 173-180). Istanbul: Istanbul technical university.
Poblete, B., & de la, C. I. (2011). Genetic algorithms as a design strategy in architecture. Unpublished doctoral dissertation, Polytechnic University of Catalonia, Barcelona.
Rafiq, Y., & Rustell, M. (2013). Building information modeling steered by evolutionary computing, Journal of Computing in Civil Engineering. Computing in Civil Engineering, 28(4), 1-11.
Rapone, G., & Saro, O. (2012). Optimisation of curtain wall facades for office buildings by means of PSO algorithm. Energy and Buildings, 45, 189-196.
RazaviAlavi, S., & AbouRizk, S. (2017). Site layout and construction plan optimization using an integrated genetic algorithm simulation framework. Computing in civil engineering, 31(4), 1-10.
Rechenberg, I. (1971). Evolutionsstrategie - Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Unpublished doctoral dissertation, Technical University of Berlin, Berlin.
Rodrigues, E., Gaspa, A. R., & Gomes, A. (2013). Evolutionary strategy enhanced with local search technique for the space allocation problem in architecture Part 1: methodology. Computer-aided design, 45, 887-897.
Rosenman, M.A. (1997) Thegeneration of form using an evolutionary approach. In: D. Dasgupta,&Z. Michalewicz (Eds), Evolutionary algorithms in engineering applications. (Vol.1, pp 69-85). Berlin: Springer.
Shih-Hsin, L., & Gerber, D. J. (2014). Multidisciplinary design optimization and performance boundaries for design decision support. Energy and Buildings, 84, 426-441.
Sakamoto, Y., Nagaiwa, A., Kobayasi, S., & Shinozaki, T. (1999). An optimization method of district heating and cooling plant operation based on genetic algorithm In ASHRAE Annual Meeting, June 19-23, (pp. 104-115). ASHRAE Transactions.
Schnier, T., & Gero, J. S. (1996). Learning genetic representations as alternative to hand-coded shape grammars. Artificial intelligence in design, 96, 39–57.
Serag, A., Ono, S., & Nakay, S. (2008). Using interactive evolutionary computation to generate creative building designs. Artificial Life and Robotics, 13(1), 246–250.
Sivanandam, S. N., & Deepa, S. N. (2008). Genetic algorithms In Introduction to genetic algorithms(pp. 15-37). Berlin: Springer.
Soddu, C. (2005). Argenìa, Artificial DNA and Visionary VariationsInSIGraDi 2004.Proceedings of the 8thIberoamerican Congress of Digital Graphics.november 10-12, Porto Alegre: Workshop e Palestras
Sönmez, N. O. (2015). Architectural layout evolution through similarity-based evaluation. Architectural computing, 13(3-4), 271 – 279.
Su, Z., & Yan, W. (2015). A fast genetic algorithm for solving architectural design optimization problems. Artificial intelligence for engineering design analysis and manufacturing, 29(4), 457- 469.
Talbourdet, F., Michel, P., Andrieux, F., & Millet, J.-R. (2013). A knowledge-aid approach for designing high-performance buildings. Building Simulation, 6(4), 337–350.
Thakur, M. K., Kumari, M., & Das, M. (2010). Architectural layout planning using genetic algorithms In3rd International conference on computer science and information technology. July 9-11, (Vol.4, pp. 5–11). Beijing: Beijing University of Technology.
Virirakis, L. (2003). GENETICA: A computer language that supports general formal expression with evolving data structures. Transactions on evolutionary computation, 7(5), 456–481.
Wang, B., & Malkawi, A. (2015). Genetic algorithm based building form optimization study for natural ventilation potentialin14th conference of international building performance simulation association. December 7-9, (pp. 640 – 647).Hyderabad, India.
Wang, W., Rivard, H., & Zmeureanu, R. (2006). Floor shape optimization for green building design. Advanced engineering informatics, 20(4), 363-378.
Wetter, M., & Wright, J. (2004). A comparison of deterministic and probabilistic optimization algorithms for nonsmooth simulation-based optimization. Building and environment, 39(8), 989-999.
Wong, S. S., & Chan, K. C. (2009). EvoArch: An evolutionary algorithm for architectural layout design. Computer-Aided Design, 41(9), 649-667.
Wright, J., & Alajmi, A. (2016). Efficient genetic algorithm sets for optimizing constrained building design problem. Sustainable built environment, 5(1), 123-131.
Yang, X. S., & Karamanoglu, M. (2013). 1 - Swarm intelligence and bio-inspired computation: an overview In X. Yang, Z. Cui, R. Xiao, A. H Gandomi., & M. Karamanoglu (Eds.), Swarm intelligence and bio-inspired computation-theory and applications (pp. 3-23). London: Elsevier.
Yazici, S. (2016). A parametric landscape urbanism method: The search for an optimal solution, A/Z ITU Journal of the faculty of architecture. 13(3), 155-16.
Yi, H. (2014). Automated generation of optimised building envelope: simulation based multiobjective process using evolutionary algorithm. Sustainablebuilding technology and urban development, 5(3), 159 -170.
Youssef, A. M., Zhai, Z., & Reffat, R. M. (2018). Generating proper building envelopes for photovoltaics integration with shape grammar theory. Energy and buildings, 158, 326-341.
Zanakis, S. H., & Evans, J. R. (1981). Heuristic “Optimization”: Why, When, and How to Use It. INFORMS Journal on Applied Analytics, 11(5), 84-91.
Zhang, L., Zhang, L., & Wang, Y. (2016). Shape optimization of free-form buildings based on solar radiation gain and space efficiency using a multi-objective genetic algorithm in the severe cold zones of China. Solar energy, 132, 38-50.
Zhang, X., & Shiu, Y. Y. (2015). A directional mutation operator for differential evolution algorithms. Applied soft computing, 30, 529-548.
Zhang, Y., Wang, S., & Ji, G. (2015). A Comprehensive survey on particle swarm optimization algorithm and its applications. Mathematical problems in engineering, 1-38.
_||_