بررسی کارایی روشهای بهینه سازی تکاملی در دستیابی به اهداف معماری و ساخت

محورهای موضوعی : معماری

مریم صادقیان ¹ ( دانشجوی کارشناسی ارشد دانشکده معماری و شهرسازی، دانشگاه فردوسی مشهد، مشهد، ایران )
اکرم حسینی ² ( استادیار دانشکده معماری و شهرسازی، دانشگاه فردوسی مشهد، مشهد، ایران. )

تاریخ دریافت : 1398/09/06 تاریخ پذیرش : 1399/01/26 تاریخ انتشار : 1400/01/01

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.

چکیده انگلیسی:

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.

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