Department of Hospital Office, Jiangsu Taizhou People's Hospital, Taizhou 225300, China.
School of Politics and Public Administration, Shanxi University, Taiyuan 030000, China.
J Environ Public Health. 2022 May 23;2022:5268887. doi: 10.1155/2022/5268887. eCollection 2022.
Aiming at the problem that particles cannot realize multidimensional analysis and poor global search ability, a composite particle swarm optimization algorithm is proposed, improving the accuracy of particle swarm optimization. Firstly, k-clustering is used to cluster risk management particle swarm optimization. The advantages of particle swarm optimization have to be given full play, and the risk of hospital equipment management from various aspects has to be controlled. Then, the multidimensional particle swarm is segmented to obtain an ordered multidimensional risk particle swarm set, which provides a basis for later risk prediction. Finally, through the fusion function of multidimensional risk particle swarm, the risk particle swarm set based on the clustering degree is constructed, and the optimal extreme value is obtained, so as to improve the accuracy of management risk calculation results. Through MATLAB simulation analysis, it can be seen that the composite particle swarm optimization algorithm is better than particle swarm optimization algorithm in global search accuracy and search time. Moreover, the calculation time and accuracy are better. Therefore, the composite particle swarm optimization algorithm can be used to analyze the risk of hospital equipment and effectively control the risk of hospital equipment management.
针对粒子无法实现多维分析和全局搜索能力差的问题，提出了一种复合粒子群优化算法，提高了粒子群优化的准确性。首先，采用 k-聚类对风险管理粒子群进行聚类，充分发挥粒子群优化的优势，控制医院设备管理的多方面风险。然后，对多维粒子群进行分段，得到有序的多维风险粒子群集，为后续的风险预测提供依据。最后，通过多维风险粒子群的融合函数，构建基于聚类度的风险粒子群集，得到最优极值，从而提高管理风险计算结果的准确性。通过 MATLAB 仿真分析，可知复合粒子群优化算法在全局搜索精度和搜索时间上均优于粒子群优化算法。并且，计算时间和准确性更好。因此，可以使用复合粒子群优化算法来分析医院设备的风险，并有效控制医院设备管理的风险。
