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

Area of Science:

• Building Science and Engineering
• Computational Science
• Sustainable Development

Background:

• Building performance optimization is crucial for energy efficiency, occupant comfort, and sustainability.
• Multi-objective optimization (MOO) has become a key focus in research over the last two decades.
• Advancements in computational tools and algorithms have enabled more sophisticated building performance analysis.

Purpose of the Study:

• To conduct a comprehensive bibliometric analysis of MOO for building performance from 2003-2023.
• To identify research trends, collaborative networks, and citation patterns in the field.
• To highlight key advancements and future research directions in optimizing building performance.

Main Methods:

• Bibliometric analysis of 1604 documents from the Web of Science Core Collection (2003-2023).
• Utilized bibliometric tools: CiteSpace, VoSviewer, and Bibliometrix.
• Analyzed research trends, collaborations, and citation patterns.

Main Results:

• Identified integration of optimization algorithms (Genetic Algorithms, PSO) with simulation platforms (EnergyPlus, MATLAB) and ANNs.
• Highlighted China and the US as leading contributors.
• Key research hotspots include energy consumption, thermal comfort, LCA, and simulation-based optimization.

Conclusions:

• MOO significantly enhances building performance metrics like energy efficiency, thermal comfort, IAQ, and cost-effectiveness.
• Genetic algorithms are widely adopted for complex multi-objective problems.
• Future research should focus on integrated, intelligent algorithms using real-time data and user behavior for adaptive and sustainable building optimization.