ReviewHeliyon2025
A review on multi-objective optimization of building performance - Insights from bibliometric analysis.
Review in Heliyon, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.