# Optimization design of internal space layout of three-bedroom residential apartment based on IGA and DE algorithm

## TL;DR

This paper introduces a new layout optimization model for three-bedroom apartments that improves space utilization and design efficiency using a combination of genetic and differential evolutionary algorithms.

## Contribution

A novel hybrid algorithm integrating IGA and DE is proposed to enhance global optimization and maintain diversity in residential layout design.

## Key findings

- The model achieved a 94.76% motion optimization rate and 96.6% space utilization rate.
- User satisfaction reached 94.21%, showing strong performance in meeting personalized design needs.
- The method faces challenges in high-dimensional spaces and complex constraints.

## Abstract

To solve the problems of insufficient global optimization ability and easy loss of population diversity in building interior layout design, this study proposes a novel layout optimization model integrating interactive genetic algorithm and improved differential evolutionary algorithm to improve the global optimization ability and maintain population diversity in building layout design. The model characterizes room functions and spatial locations through binary coding, and uses dynamic fitness function and backtracking strategy to improve space utilization and functional fitness. In the experiments, optimization metrics such as kinematic optimization rate (calculated based on the shortest path and connectivity between functional areas), space utilization rate (calculated by the ratio of room area to total usable space), and functional fitness (based on the weighted sum of users’ subjective evaluations and functional matches) all perform well. Quantitatively, it is found that the model achieves 94.76% in terms of motion optimization rate, the highest space utilization rate is 96.6%, functional fitness is 9.4, and user satisfaction is close to 94.21%. The optimization results show that the proposed method has significant advantages in improving space utilization and meeting personalized design needs. However, despite the good optimization results, the method still faces the problem of improving the optimization ability under high-dimensional space and complex constraints. This study provides an efficient solution for intelligent building layout design and has certain practical value.