Genetic algorithm-based multi-objective optimisation for energy-efficient building retrofitting: A systematic review
Konstantinos Alexakis, Vasilis Benekis, Panagiotis Kokkinakos, Dimitris Askounis
Abstract
• NSGA-II is the most applied algorithm for multi-objective building retrofitting. • Rising use of life-cycle analysis addresses embodied energy and environmental impacts. • EnergyPlus integration with GAs enhances simulation and optimisation efficiency. • Studies reveal gaps in addressing occupant preferences and climate variability impacts. The operation of buildings accounts for approximately 40% of global energy consumption and 36% of greenhouse gas emissions. Retrofitting existing buildings has become essential to mitigating these impacts, yet the process presents complex optimisation challenges that require balancing conflicting objectives, such as minimising energy consumption, reducing costs, and improving thermal comfort. This systematic review investigates the application of Genetic Algorithms (GAs) to building retrofits, with a particular focus on their use in multi-objective optimisation. By analysing 175 studies − 118 new publications and 57 from a prior review – this work identifies key trends, challenges, and advancements in GA-based retrofitting approaches. The findings reveal that NSGA-II remains the most widely adopted GA, demonstrating strengths in computational efficiency and solution quality. An increasing integration of life-cycle analysis and dynamic simulation tools, such as EnergyPlus, reflects the growing sophistication of optimisation strategies. However, significant challenges persist, including prolonged computation times, a lack of open data, and limited consideration of occupant preferences and indoor environmental quality. This review systematically highlights these limitations in optimisation formulation, such as stakeholder conflicts, model sensitivity to climate variability, and scalability across different building types. By presenting these insights, this review offers a practical framework for researchers and practitioners to navigate the complexities of GA-based optimisation for building retrofitting. Recommendations include enhancing the accessibility of tools, incorporating emerging materials and technologies, and addressing climate change through tailored retrofit simulations. This work aims to advance the theoretical and practical application of GAs, contributing to the development of efficient, scalable, and equitable retrofit strategies.