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首页> 外文期刊>International Journal of Innovative Computing Information and Control >CYBER SWARM ALGORITHMS FOR MULTI-OBJECTIVE NURSE ROSTERING PROBLEM
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CYBER SWARM ALGORITHMS FOR MULTI-OBJECTIVE NURSE ROSTERING PROBLEM

机译：多目标护士名册问题的网络群算法

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摘要

It is time-consuming to determine the nurse rostering using traditional human-involved manner in order to account for administrative operations, business benefits, governmental regulations, and nurse requests. Moreover, the objectives cannot be measured quantitatively even when the nurse rostering is generated after a lengthy manual process. This paper presents a multiobjective optimization method based on the cyber swarm algorithm (CSA), one of the most successful variations of particle swarm optimization, to solve the nurse rostering problem with multiple objectives and a set of hard constraints. The proposed method incorporates salient features from particle swarm optimization, adaptive memory programming, scatter search and path relinking to create advantages. The experimental results on two test datasets demonstrate that the proposed method outperforms several state-of-the-art multiobjective evolutionary algorithms in obtaining results with better convergence and diversity performances.
机译：使用传统的人工干预方式确定护士名册是很费时的，以便考虑行政操作，业务收益，政府法规和护士的要求。此外，即使经过漫长的手动过程生成护士名册，也无法定量测量目标。本文提出了一种基于网络群算法（CSA）的多目标优化方法，该方法是粒子群优化最成功的变体之一，它解决了具有多个目标和一组硬约束的护士排班问题。所提出的方法结合了粒子群优化，自适应存储器编程，散点搜索和路径重新链接的显着特征，从而创造了优势。在两个测试数据集上的实验结果表明，该方法在获得具有更好的收敛性和多样性性能的结果方面，胜过几种最新的多目标进化算法。

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