Energy-saving application of multi-objective particle swarm optimization algorithm in low-carbon interior design

Abstract

To address the challenge of synergistically optimizing multiple conflicting objectives, energy efficiency, low carbon emissions, and occupant comfort, in indoor design, this study proposes an improved multi-objective particle swarm optimization (IMOPSO) framework. Tailored to the mixed heterogeneous nature of design variables, the method introduces a probability-driven discrete-variable update mechanism, integrated with adaptive parameter tuning and a dual-archive elitist guidance strategy, enabling automated, iterative coupling with building energy simulation tools. A case study based on a typical office space demonstrates that, compared with NSGA-II, MOEA/D, and standard MOPSO, the Pareto front obtained by IMOPSO achieves better performance in generation distance (0.019) and hypervolume (0.745), with solution-set uniformity improved by approximately 13%. Analysis of optimized solutions reveals that, while maintaining equivalent thermal comfort, operational energy consumption can be reduced by 31.2% and whole-life-cycle carbon emissions by 22.5%. This study provides a quantitative decision-support tool for performance-driven, low-carbon indoor design.