A review on multi-objective optimization of building performance - insights from bibliometric analysis

The optimization of building performance has gained significant attention over the past two decades, driven by the need for energy efficiency, occupant comfort, and environmental sustainability. This paper conducts a comprehensive bibliometric analysis of multi-objective optimization (MOO) for building performance, spanning research publications from 2003 to 2023. Utilizing advanced bibliometric tools such as CiteSpace, VoSviewer, and Bibliometrix, we analyzed 1604 documents from the Web of Science Core Collection to map collaborative networks, research trends, and citation patterns. The study identifies notable advancements in the integration of sophisticated optimization algorithms, including genetic algorithms and particle swarm optimization (PSO), with simulation platforms like EnergyPlus and MATLAB, while utilizing Artificial Neural Networks (ANN) for enhanced predictive capabilities. These integrations have markedly enhanced the efficiency and accuracy of optimizing key building performance metrics, including energy efficiency, thermal comfort, indoor air quality (IAQ), and cost-effectiveness. China and the United States emerged as leading contributors, with higher education institutions playing a critical role in research outputs. Key research hotspots identified include energy consumption, thermal comfort, life cycle assessment, and simulation-based optimization. The effectiveness of genetic algorithms in managing complex multi-objective problems has led to their widespread adoption. Looking forward, future research is anticipated to focus on the development of more integrated and intelligent optimization algorithms that leverage real-time data and user behavior, thus improving the adaptability and sustainability of building performance optimization. This study provides a detailed insight into the evolution and current trends in MOO research, laying a strong foundation for future investigations aimed at advancing building performance in the context of energy efficiency and environmental sustainability.

Text 127713.pdf - Published Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (11MB) Official URL or Download Paper: https://linkinghub.elsevier.com/retrieve/pii/S2405...

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