基于改进粒子群算法的应急资源优化调控方法研究

Research on optimization and regulation of emergency resources based on improved Particle Swarm Optimization algorithm

针对目前医疗机构人力资源管理策略应对突发事件能力不足的问题,文中提出了一种基于改进粒子群算法的应急资源优化调控方法。该方法利用模拟退火算法的Metropolis准则和人工鱼群算法的拥挤因子对粒子群算法进行改进,弥补了该算法全局搜索能力较差及容易陷入局部极值的问题,从而提高了算法寻找最优参数的能力。仿真实验证明,改进粒子群算法的决定系数R2比改进前增加了7.74%,与其他算法的对比实验则验证了所提算法的优越性,且相比于表现最优的极限学习机R2提升了5.43%。因此,所提出的应急资源优化调控方法可以有效应对医疗机构所面临的突发事件,并为人员优化配置提供理论支持。

Aiming at the problem that the human resource management strategy of medical institutions is insufficient to deal with emergencies,this paper proposes a method of emergency resource optimization and regulation based on improved Particle Swarm Optimization algorithm.By using the Metropolis criterion of simulated annealing algorithm and the crowding factor of artificial fish swarm algorithm to improve the particle swarm algorithm,this method solves the problems of poor global search ability of particle swarm algorithm and easy to fall into local extremum,thus improving the ability of particle swarm algorithm to find the best parameters.The simulation experiment shows that the decision 2 coefficient R of the improved PSO algorithm is 7.74%higher than that before the improvement.The comparison experiment with other algorithms verifies the superiority of the proposed algorithm,which is 5.43%higher than that of the optimal extreme learning machine.Therefore,the proposed optimization and regulation method of emergency resources can effectively deal with emergencies in medical institutions,providing theoretical support for personnel optimization.