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<p>Interior design plays a pivotal role in enhancing the quality, functionality, and sustainability of built environments. However, balancing multiple conflicting objectives—such as aesthetic appeal, occupant health, energy efficiency, and sustainability cost—poses a significant challenge. This study proposes a novel hybrid optimization approach combining the harmony search algorithm (HSA) and teaching–learning-based optimization (TLBO) to address this multi-objective interior design problem. A mathematical model incorporating seven key decision variables was formulated to evaluate four performance criteria: aesthetic appeal score, health index, sustainability cost, and energy efficiency. The hybrid HSA–TLBO algorithm was applied to a smart office case study, generating 19 Pareto-optimal solutions. Post-Pareto analysis using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was employed to identify the most suitable configuration. The results reveal that the proposed hybrid algorithm offers superior convergence, diversity, and solution quality compared to standalone metaheuristics and other multi-objective algorithms such as MOACO, NSGA-III, and MOPSO. The study further validates the practicality of selected solutions through radar charts and sensitivity analysis. This research provides a robust decision-making framework for architects and designers to optimize interior environments across performance, health, cost, and energy dimensions.</p>

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Interior design optimization in construction projects using HSA–TLBO: a hybrid multi-objective algorithm for aesthetic appeal, health, sustainability cost, and energy efficiency

  • 期刊论文

  • 发表日期：

    2025年5月7日

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  • A. R. Palaniappan,
  • Sridhara Thenkmane,
  • K. V. Shivakumar

摘要

Interior design plays a pivotal role in enhancing the quality, functionality, and sustainability of built environments. However, balancing multiple conflicting objectives—such as aesthetic appeal, occupant health, energy efficiency, and sustainability cost—poses a significant challenge. This study proposes a novel hybrid optimization approach combining the harmony search algorithm (HSA) and teaching–learning-based optimization (TLBO) to address this multi-objective interior design problem. A mathematical model incorporating seven key decision variables was formulated to evaluate four performance criteria: aesthetic appeal score, health index, sustainability cost, and energy efficiency. The hybrid HSA–TLBO algorithm was applied to a smart office case study, generating 19 Pareto-optimal solutions. Post-Pareto analysis using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was employed to identify the most suitable configuration. The results reveal that the proposed hybrid algorithm offers superior convergence, diversity, and solution quality compared to standalone metaheuristics and other multi-objective algorithms such as MOACO, NSGA-III, and MOPSO. The study further validates the practicality of selected solutions through radar charts and sensitivity analysis. This research provides a robust decision-making framework for architects and designers to optimize interior environments across performance, health, cost, and energy dimensions.