Key points are not available for this paper at this time.
Improving the energy efficiency of buildings is a critical pathway for mitigating greenhouse gas emissions and fostering sustainable urban development. This study introduces a simulation-based multi-objective optimization framework designed to enhance both the thermal and economic performance of residential buildings. A representative single-family dwelling located in Patras, Greece, served as a case study to demonstrate the application and scalability of the proposed methodology. The optimization simultaneously minimized two conflicting objectives: the building’s annual thermal energy demand and the cost of construction materials. The computational process was implemented using MATLAB’s Multi-Objective Genetic Algorithm, supported by a modular Excel interface that enables dynamic customization of design parameters and climatic inputs. A parametric analysis across four optimization scenarios was conducted by systematically varying the key algorithmic hyperparameters—population size, mutation rate, and number of generations—to assess their impact on convergence behavior, Pareto front resolution, and solution diversity. The results confirmed the algorithm’s robustness in producing technically feasible and non-dominated solutions, while also highlighting the sensitivity of optimization outcomes to hyperparameter tuning. The proposed framework is a flexible, reproducible, and computationally tractable approach for supporting early-stage, performance-driven building design under realistic constraints.
Makris et al. (2025) studied this question.