Applying genetic algorithms to space optimization decision of farmland bio-energy intensive utilization

The development of bio-energy intensive utilization of farmland is to solve China's emerging issues related to energy and environment in an important way. Given the spatial distribution of bio-energy is scattered, not continuous, the intensive utilization of farmland bio-energy is different from that of the traditional energy, i.e. coal, oil, natural gas, etc.. The estimation of biomass, the spatial distribution and the space optimization study are the key for practical applications to develop bio-energy intensive utilization. Based on a case study conducted in Guangdong province, China, this paper provides a framework that estimates available biomass and analyzes its distribution pattern in the established NPP model quickly; it also builds the primary collection ranges by Thiessen polygon in different scales. The application of Genetic Algorithms (GA) to the optimization and space decision of bio-energy intensive utilization is one of the key deliveries. The result shows that GA and GIS integration model for resolving domain-point supply and field demand has obvious advantages. A key finding presents that the model simulation results have enormous impact by the MUAP. When Thiessen polygon scale with 10 KM proximal threshold is established as the primary collecting scope of bioenergy, the fitness value can be maximized in the optimized process. In short, the optimized model can provide an effective solution to farmland bio-energy spatial optimization.