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<p>The optimization of construction retrofitting projects requires balancing multiple, often conflicting objectives, including project duration, cost, indoor air quality (IAQ), and CO₂ emissions. Traditional optimization methods face challenges in addressing such many-objective problems while ensuring solution diversity and convergence. This study proposes a novel algorithm, chaotic mapping initialization integrated with NSGA-III (CMI–NSGA-III), to enhance performance in construction retrofitting optimization. The model formulates retrofitting decisions using binary variables, incorporating project constraints on budget, resources, and carbon emission limits. Four objectives are defined: minimization of time, cost, and emissions, and maximization of IAQ. The integration of chaotic mapping, through logistic-based initialization, improves exploration capability, reduces premature convergence, and enhances the quality of Pareto-optimal solutions. Validation against real-world retrofitting projects demonstrates reliable prediction accuracy, with mean absolute percentage error (MAPE) values of 4.8% for time, 2.9% for cost, 2.5% for IAQ, and 3.2% for CO₂. Comparative analysis with NSGA-III, MOPSO, and MOACO shows that CMI–NSGA-III outperforms competitors in convergence, spread, and hypervolume metrics. The results provide project managers with a robust decision-support tool, enabling balanced trade-offs among economic, environmental, and technical dimensions in sustainable construction retrofitting.</p>

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Chaotic mapping initialized NSGA-III for multi-objective optimization of ventilation retrofitting in construction projects: balancing time, cost, indoor air ouality, and CO₂ emissions

  • 期刊论文

  • 发表日期：

    2025年9月29日

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  • Apurva Sharma,
  • Anupama Sharma,
  • Priyanka Gupta,
  • Shubham Mishra

摘要

The optimization of construction retrofitting projects requires balancing multiple, often conflicting objectives, including project duration, cost, indoor air quality (IAQ), and CO₂ emissions. Traditional optimization methods face challenges in addressing such many-objective problems while ensuring solution diversity and convergence. This study proposes a novel algorithm, chaotic mapping initialization integrated with NSGA-III (CMI–NSGA-III), to enhance performance in construction retrofitting optimization. The model formulates retrofitting decisions using binary variables, incorporating project constraints on budget, resources, and carbon emission limits. Four objectives are defined: minimization of time, cost, and emissions, and maximization of IAQ. The integration of chaotic mapping, through logistic-based initialization, improves exploration capability, reduces premature convergence, and enhances the quality of Pareto-optimal solutions. Validation against real-world retrofitting projects demonstrates reliable prediction accuracy, with mean absolute percentage error (MAPE) values of 4.8% for time, 2.9% for cost, 2.5% for IAQ, and 3.2% for CO₂. Comparative analysis with NSGA-III, MOPSO, and MOACO shows that CMI–NSGA-III outperforms competitors in convergence, spread, and hypervolume metrics. The results provide project managers with a robust decision-support tool, enabling balanced trade-offs among economic, environmental, and technical dimensions in sustainable construction retrofitting.