سروش حسن زاده

In recent years, architectural form optimization aimed at enhancing environmental performance an‎d reducing energy consumption has emerged as a central focus in architectural an‎d building engineering research. In this context, the integration of evolutionary algorithms an‎d parametric modeling enables more efficient exploration of the design space an‎d facilitates the identification of optimal solutions. This study investigates the effectiveness of a computational framework based on genetic algorithms, with an emphasis on multi-objective optimization an‎d the use of quantitative metrics related to daylight performance an‎d sustainability criteria. The research methodology involves the development of a three-dimensional parametric model, the definition of objective functions an‎d penalty functions aligned with daylight requirements an‎d geometric constraints, an‎d the execution of the optimization process within a dynamic daylight simulation environment. Input data were analyzed through diverse optimization scenarios using specialized software tools. The genetic algorithm was implemented with a ran‎domly generated initial population an‎d employed selec‎tion, mutation, an‎d crossover operators across multiple successive generations to achieve convergence toward optimal solutions. Findings indicate that the application of a daylight-based penalty function led to an approximate 23% reduction in average underlit areas an‎d a 15% improvement in light distribution uniformity compared to the baseline model. Furthermore, curved shading devices demonstrated superior performance in sustainability metrics an‎d achieved higher scores in LEED eva‎luations. These results affirm that the synergy between parametric modeling, evolutionary algorithms, an‎d penalty function implementation can serve as an effective an‎d evidence-based approach in architectural design decision-making processes.

فرم‌يابي , نور روز , تابع جريمه , بهينه‌سازي‌

: Form-finding , Daylight , Penalty Function , Optimization

Soroush Hassanzadeh

Dr. Abbas Tarkashvand, Dr. Haniyeh Sanaieian