Spatial Layout Optimization in Urban Renewal Based on Improved NSGAII Algorithm

A multi-objective optimization method based on the improved non-dominated sorting genetic algorithm II was proposed to address the problem of spatial layout optimization in urban renewal. The study first constructed an urban spatial layout model with net zero carbon as the core concept, setting three optimization objectives: minimizing net carbon emissions, maximizing regional GDP, and compact utilization of land functions. By introducing the Non-dominated Sorting Genetic Algorithm II for multi-objective optimization of the solution, this algorithm uses fitness non-dominated sorting and crowding distance calculation to maintain population diversity and combined the approximate ideal solution sorting method to improve convergence. The experiment outcomes indicate that the raised algorithm achieves an optimization result of 5.79 × 10−20 in the Rastrigin function and exhibits better uniformity in the distribution of solution values in the ZDT1 function. In terms of urban spatial layout, the optimized scheme has a net carbon emission of 19,821.80 tons, a regional GDP of 2.342367 billion USD, and a compact land function of 5791.93, indicating that the scheme not only effectively controls carbon emissions but also demonstrates the rationality and sustainability of land resource use.