A conceptual framework for multi-objective optimization of building performance: Integrating intelligent algorithms, simulation tools, and climate adaptation

This study systematically examined recent research trends in multi-objective optimization (MOO) for building performance from 2020 to 2024 and proposed a conceptual framework integrating intelligent algorithms, simulation tools, and climate adaptation strategies. A thematic analysis of 40 peer-reviewed articles was conducted using ATLAS.ti, revealing three dominant research themes: intelligent algorithms, building performance simulation techniques, and adaptive design for climate change. Quantitative analysis highlights China’s prominent contributions to the field, while the thematic analysis reveals three key findings: (1) optimization methods based on intelligent algorithms such as NSGA-II, artificial neural networks, and gradient-boosted decision trees significantly enhance computational efficiency; (2) dynamic simulation integrated with lifecycle assessment enables a more comprehensive evaluation of building performance; and (3) climate-adaptive strategies improve building resilience to future climate uncertainties. Based on these insights, the proposed framework combines these three components to achieve computational efficiency, maintain accuracy, and improve adaptability. The framework provides a systematic, data-driven approach to address trade-offs among energy efficiency, thermal comfort, and indoor air quality. Its practical value is demonstrated through applications in residential, educational, and commercial buildings across various climate zones. These case studies highlight the framework’s capability to guide high-performance, sustainable, and climate-responsive building design. Furthermore, this review identifies future research priorities, including the integration of dynamic simulation with real-time optimization, and the development of lifecycle-oriented, comprehensive evaluation systems to address emerging environmental challenges. Overall, the study contributes a holistic perspective and actionable methodology for advancing intelligent, climate-adaptive building performance optimization.

Text 120154.pdf - Published Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (1MB) Official URL or Download Paper: https://malque.pub/ojs/index.php/mr/article/view/8...

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