内容：
应急物资的调度是一个典型的社会服务空间优化问题,将粒子群优化算法与GIS相结合建立应急物资空间分配智能优化模型。首先,针对传统标准粒子群算法随维度增高而极易陷入局部最优的特点,模型将粒子群进化曲线的曲率定义为种群多样性,采用"扑食"和"探索"2个状态建立自适应双态进化机制;其次,改进的算法只针对全局最优粒子进行变异,很好地控制了群体"集群飞行"与"外空探索"之间的协调;第三,利用动态递归和生物智能的随机特性建立起了约束处理机制。通过低维和高维理论模型测试,验证了模型在低维优化空间获取了绝对最优解;而在高维空间也达到了非常高的优化精度。最后,选择某市物资供应为案例,利用该模型分析了试验区物资分配格局。研究表明,耦合生物智能的GIS空间优化模型在智慧城市建设中具有重要的应用意义。 The scheduling of emergency materials is a typical social service space optimization problem, and the particle swarm optimization algorithm is combined with GIS to establish the intelligent optimization model of emergency material space allocation. Firstly, the curvature of the particle evolution curve is defined as the diversity of the population, and the fitness is established by using the "standard" and "exploratory" two states for the traditional standard particle swarm algorithm with the dimension increase. The improved algorithm is only for the global optimal particle variation, well control the group "cluster flight" and "outer space exploration" between the coordination; third, the use of dynamic recursion and biological intelligence The stochastic feature establishes the constraint processing mechanism. The low - dimensional and high - dimensional theoretical model tests show that the model obtains the absolute optimal solution in the low - dimensional optimization space and achieves very high optimization accuracy in the high - dimensional space. Finally, the paper chooses the material supply of a city as a case, and uses this model to analyze the distribution pattern of material in the test area. The research shows that the GIS spatial optimization model of coupled bio-intelligence has important application significance in intelligent city construction.

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