A tool for energy-efficient building optimization using swarm intelligence algorithms in the early design phase

Depletion of energy resources has become a global concern, necessitating the reduction of energy consumption. Given that the construction process is one of the main contributors to energy use, it is imperative to develop methods that enhance energy efficiency throughout the entire building lifecycle, from the design stage to occupancy. This study adopts an approach that prioritizes energy efficiency in architectural design. Although simulation tools developed to evaluate energy performance exist, their widespread adoption remains limited. This is largely due to a continued reliance on traditional design methods and the perception that energy calculations impose an additional burden on the design process. The aim of this study is to develop a tool that provides designers with rapid and effective design alternatives. In this context, parametric/algorithmic design and energy optimization methods have been utilized, with special attention given to zoning constraints in residential buildings. In the study, swarm intelligence algorithms—Artificial Bee Colony (ABC) and Tree-Seed Algorithm (TSA)—were implemented. A normative tool integrating generative design, energy performance calculations, and optimization was developed within the MATLAB environment. To test the reliability and accuracy of the developed tool, a case study was conducted involving three residential buildings of different scales: two-story, five-story, and ten-story structures. The energy consumption of existing buildings was compared with the energy consumption of alternative designs generated using the developed tool. The results demonstrated a reduction in energy consumption of 11.51% in the two-story building, 5.93% in the five-story building, and 15.66% in the ten-story building. When comparing the optimization durations and energy performance improvement rates of the two algorithms, it was found that they produced consistent and similar outcomes. Overall, the Artificial Bee Colony algorithm exhibited the best performance. The developed normative tool significantly contributes to environmental sustainability by aiming to increase energy efficiency in architectural design processes. The core principle guiding its development is the adoption of approaches that consider energy efficiency from the early design phases; additionally, the tool is supported by a user-friendly interface. In the future, the tool can be adapted to various climate conditions and building typologies, and if integrated with artificial intelligence technologies, it has the potential to become an indispensable decision-support system for designers. Moreover, the system can be utilized by urban planners and holds the potential to play a guiding role in urban planning processes by promoting energy efficiency.