Design of Indoor Space Layout Based on Improved Gannet Optimization Algorithm

Indoor space layout optimization is important in improving spatial efficiency and accommodating human behaviour. Nevertheless, existing literature on optimal indoor room allocation is scarce. To overcome this issue, this research presents an integrative methodology through the Improved Gannet Optimization Algorithm (IGOA) for optimization of indoor space layout design. This approach integrates room allocation optimization with indoor connectivity map development. Through the use of parametric performance simulations, the IGOA-based process helps building designers make energy-conserving choices during the initial stages of the design process. IGOA improves functional connectivity through adjacency constraints that allow better availability and flow of movement within the layout. Designers are thus enabled to incorporate energy-saving strategies from the very beginning, which promoting innovative and sustainable spatial configurations. The efficacy of the proposed IGOA was tested through several error measures like Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-Squared <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathbf{R^{2}})$</tex>. The IGOA achieves less MAE of 0.318, MSE of 0.005 and R2 of 0.624 for light environment which is better than baseline models.