The Impact of Dual Objective Optimization Model Combining Non-Dominated Genetic Algorithm on Rural Industrial Ecological Economy

Due to the development of industrial economy, it has caused serious damage to the ecological environment. Based on the industrial structure and production scale, rural industrial economic parks are planned to analyze the quantity and weight of pollutants emitted from the original industries. The results showed that the quantity and weight of hydrogen sulfide in the coking industry were 10kg/t and 94, respectively. The weight of smoke and carbon monoxide in the steelmaking industry was relatively high, with 54 and 34, respectively. Non-dominated sorting genetic algorithm and multi-objective programming model are used to optimize the comprehensive benefits and industrial structure of rural industrial ecological economy. According to the experimental results, when the scale of the coking industry was 135600 tons, the steelmaking industry was 314900 tons, the ironmaking industry was 148100 tons, and the underground coal gasification industry was 424.76 million Nm3. The comprehensive economic benefits of the industry reached the optimal level of 0.6415. The environmental and comprehensive benefits generated by the increased power generation industry were 64.98 and 40.87, respectively. Therefore, it indicates that the dual objective programming model combining non-dominated sorting genetic algorithm can improve the rural industrial ecological economy.

@article{Wang2024,
  title     = {The Impact of Dual Objective Optimization Model Combining Non-Dominated Genetic Algorithm on Rural Industrial Ecological Economy},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {5},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Ying Wang},
  doi       = {10.14569/IJACSA.2024.0150528},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150528}
}