Application of Genetic Algorithm in Optimization Simulation of Industrial Waste Land Reuse

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Peng Bai
Yunan Zhao
Junjia Chang


In order to better understand the application of optimization simulation for industrial waste land reuse, the author proposes an application study based on nonlinear genetic algorithm in the optimization simulation of industrial waste land reuse. The author takes the landscape renovation and reuse of industrial waste sites as the research object, and through research on the current situation of landscape renovation and reuse of industrial waste sites both domestically and internationally, as well as on-site inspections, attempts to use landscape design techniques to deal with this once glorious but destructive industrial landscape that has already declined. Secondly, a genetic algorithm for enhancing the timeliness of industrial waste land reuse is proposed, which is based on random walks, combine users' long-term and short-term preferences to calculate the most suitable Top-N industrial waste land reuse optimization model for the current period. Finally, the two algorithms proposed by the author were experimentally validated on the dataset. In the CiteU Like dataset, the best performance was achieved at a=0.4, while in the JD dataset, the best performance was achieved at a=0.6. When k=6, the hit rate significantly decreases by about 50%. The URT-R genetic algorithm exhibits a high recommendation hit rate in recommendations targeting timeliness. The author analyzed the different characteristics of industrial waste reuse in scenic areas and optimized their essence and transformation methods, further improving the transformation and renewal methods of industrial waste land in the process of urban development in China. I hope to provide useful references for future research on related topics and practices.

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Special Issue - Deep Learning-Based Advanced Research Trends in Scalable Computing