ORIGINAL RESEARCH article
Front. Built Environ.
Sec. Sustainable Design and Construction
Volume 11 - 2025 | doi: 10.3389/fbuil.2025.1526209
Enhancing Sustainable Urban Design Using Machine Learning: Comparative Analysis of Seven Metaheuristic Algorithms in Energy-Efficient Digital Architecture
Provisionally accepted
- Al-Ahliyya Amman University, Amman, Jordan
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Metaheuristics optimization algorithms used in this research include PSO, ACO, GA, and SA and represent effective approaches toward reaching an optimal or near-optimal solution for decision variables with an intention to improve energy efficiency, increasing indoor comfort whilst reducing the carbon footprint of building operations. These algorithms will be applied across different sizes of a synthesized data pool prepared with important features like window-to-wall ratio, efficiency in HVAC systems, renewable systems integration, among others. PSO demonstrated the best scenario in both cases with an optimum convergence rate of 24.1%; ACO produced almost the same best result as mentioned with high rates of reductions in carbon footprints. These had their best energy-savings while convergence in GA and SA was extremely slow with energy efficiencies that turned out to be pretty low. This could allow real proposals to architects, urban planners, and policy thinkers in order for them to turn such ideal cases into viable realities within sustainable development initiatives.
Keywords: Sustainable Building Design, Metaheuristic algorithms, multi-objective optimization, energy efficiency, Carbon footprint reduction, Occupant comfort
Received: 11 Nov 2024; Accepted: 27 May 2025.
Copyright: © 2025 Ghoneim and Arabasy. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Rehab Ghoneim, Al-Ahliyya Amman University, Amman, Jordan
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