Multi-objective intelligent algorithm model design for housing environment optimization

With the improvement of the national living standard, the buyers have higher and higher requirements for the rationality and aesthetics of the spatial planning and layout of the residential area. The traditional residential space planning method is purely manual design, which is inefficient, and the design effect will be greatly affected by the designer’s work experience and personal aesthetics. Therefore, this research attempts to combine Pareto solution set and piecewise prediction idea into genetic algorithm, propose an algorithm for solving multi-objective optimization problems, and build an intelligent housing environment planning system based on this. The statistical results of simulation experiments show that the system can output more design schemes with better overall quality than the comparison system and manual planning results, and the stability of multiple operations is higher. When the number of iterations reaches 200, the average value of Pareto optimal solution number and optimal solution quality index QPS of the former is 44 and 0.41 respectively. The expert group analyzed the design results of this method and manual method for an actual case, and found that the results designed by this method met the requirements and the calculation efficiency was much faster than manual processing. From the simulation test data and the actual case analysis, it can be seen that the intelligent housing environment planning system designed in this study is helpful to improve the efficiency of residential space design and the stability of residential space scheme style.