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<p>This study presents a novel hybrid optimization framework integrating NSGA-III and multi-objective teaching-learning-based optimization (MOTLBO) to optimize energy consumption, life-cycle cost, and carbon emissions in modular steel buildings. The research aims to support net-zero energy building (NZEB) compliance by identifying optimal design configurations that balance performance, cost, and sustainability. A comprehensive case study of a two-story pre-engineered steel office building in New Delhi, India, is used to demonstrate the methodology. The optimization process explores multiple design variables, including insulation type and thickness, HVAC systems, glazing, lighting, solar PV area, and building envelope characteristics. The hybrid algorithm generates a diverse Pareto front of optimal solutions, which are further refined using post-Pareto analysis techniques such as TOPSIS and k-means clustering to guide final decision-making. Among the solutions, design A16 emerges as the most effective, satisfying all major NZEB compliance metrics with an Energy Use Intensity of 48.7 kWh/m²/year and 54% renewable energy contribution. Comparative analysis against five benchmark algorithms confirms the superior convergence, diversity, and efficiency of the proposed hybrid approach. The results underscore the potential of integrated metaheuristic techniques in advancing net-zero, resource-efficient modular construction in hot and dry climates.</p>

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Hybrid NSGA-III and multi-objective TLBO for post-Pareto optimization of energy, cost, and carbon in modular steel buildings toward net-zero compliance

  • 期刊论文

  • 发表日期：

    2025年9月2日

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  • Shailendra Kumar Khare,
  • Anjali Gupta,
  • Devendra Vashist

摘要

This study presents a novel hybrid optimization framework integrating NSGA-III and multi-objective teaching-learning-based optimization (MOTLBO) to optimize energy consumption, life-cycle cost, and carbon emissions in modular steel buildings. The research aims to support net-zero energy building (NZEB) compliance by identifying optimal design configurations that balance performance, cost, and sustainability. A comprehensive case study of a two-story pre-engineered steel office building in New Delhi, India, is used to demonstrate the methodology. The optimization process explores multiple design variables, including insulation type and thickness, HVAC systems, glazing, lighting, solar PV area, and building envelope characteristics. The hybrid algorithm generates a diverse Pareto front of optimal solutions, which are further refined using post-Pareto analysis techniques such as TOPSIS and k-means clustering to guide final decision-making. Among the solutions, design A16 emerges as the most effective, satisfying all major NZEB compliance metrics with an Energy Use Intensity of 48.7 kWh/m²/year and 54% renewable energy contribution. Comparative analysis against five benchmark algorithms confirms the superior convergence, diversity, and efficiency of the proposed hybrid approach. The results underscore the potential of integrated metaheuristic techniques in advancing net-zero, resource-efficient modular construction in hot and dry climates.