Territorial Space Optimization Method Based on Multi-Objective Genetic Algorithm and FLUS Model

The rational planning of territorial space is related to the speed of economic development and the protection of ecology. There is an urgent need for a more advanced integrated territorial space layout method. For additional spatial layout design, the study suggests a multi-objective genetic algorithm based on spatial data prediction and coupled with land simulation modeling in the context of big data and machine learning development. The innovation of the research lies in the optimization of the multi-objective genetic algorithm by improving the crossover and mutation process of genetic operators, enhancing the adaptability of the algorithm in dynamic environments, and improving the prediction accuracy of the spatial quantity structure of the national territory. The outcomes revealed that this approach was applied to the prediction of populations of different sizes 400 and 600 with an average accuracy of 67%, which was 17% higher than that of the traditional genetic algorithm. The difference between the predicted and actual future spatial population values using the proposed algorithm was less than 2%, which was 56.5% lower than the other two prediction algorithms on average. The proposed land simulation model reached the highest accuracy at 100 iterations with an average fitness of $2.4\times 10 ^{11}$ , which was 13% and 27% higher than the other two traditional neural network algorithms, respectively. In the two simple functions of f1 and f2, the highest convergence accuracy reached $10^{-30}$ and $10^{-10}$ , respectively. In the two more complicated functions of f3 and f4, the optimal solutions were approximated in the ranges of [ $10^{2}$ , $10^{12}$ ] and [ $10^{-2}$ , $10^{6}$ ] without significant fluctuations. Therefore, the proposed algorithm can effectively predict the number of territorial space in macro and micro simulation, and has high feasibility and accuracy. This provides a reliable basis for the government to carry out land resource planning, and promotes the sustainable development of ecology and economy.