Study on the smart transformation strategy of old neighborhoods based on urban renewal

Abstract

With the rapid development of the economic level, urban renewal has become a major project in urban construction nowadays. Among the urban renewal projects, the renovation of old neighborhoods is an important part. Most of the traditional renovations only consider the cost impact, ignoring the influence of residents’ wishes and environmental factors. Therefore, an intelligent preference model for retrofitting solutions becomes crucial. This study establishes a multi-objective optimization model for the renovation of old neighborhoods under the concept of urban regeneration, keeping in mind the theme of smart cities. The study innovatively solves by optimizing a genetic algorithm to obtain the optimal solution for the renovation of old neighborhoods. Through data analysis and model testing of the renovated old neighborhoods, the results show that the method has an error of 2.04d for the renovation duration, 0.89% for the cost and 0.43% for the quality score. The method significantly improves the efficiency of the search for excellence, while the study provides a reference path for the smart retrofitting of old neighborhoods.