Metaheuristic machine learning for optimizing sustainable interior design: enhancing aesthetic and functional rehabilitation in housing projects

Abstract

The paper investigates the amalgamation of LightGBM and Enhanced Colliding Bodies Optimization (ECBO) to establish a resilient framework for sustainable interior design optimization in residential projects. The main goal is to harmonize aesthetic appeal, functionality, and energy efficiency by applying modern machine learning and metaheuristic optimization methods. LightGBM was utilized for predictive modeling of essential design outcomes, achieving good prediction accuracy, with R-squared values of 0.892 for energy savings, 0.839 for functional enhancements, and 0.782 for aesthetics. Critical elements, including sustainable materials, project budget, and energy efficiency ratings, surfaced as pivotal influences on design improvements. The ECBO further refined these design elements, yielding a 28.13% enhancement in aesthetic evaluations, a 22.86% gain in functionality, a 41.56% advancement in energy savings, and a 29.17% decrease in carbon footprint. Compared to conventional algorithms such as Particle Swarm Optimization and Genetic Algorithm, the ECBO exhibited enhanced convergence velocity and solution efficacy. This study presents a thorough, data-centric methodology for sustainable interior design, offering an efficient framework for attaining many design objectives in housing rehabilitation.