论文标题
针对员工调度问题的基于GPGPU的ACO和PSO算法的比较分析
Comparative Analysis of GPGPU based ACO and PSO Algorithm for Employee Scheduling Problems
论文作者
论文摘要
与数学算法和其他启发式优化技术相比，粒子群优化（PSO）和蚂蚁菌落优化（ACO）很简单，易于实现，控制参数的鲁棒性以及它们的计算效率。 PSO和ACO中的计算非常简单。与其他开发的计算相比，它具有更大的优化能力，并且可以轻松完成。它用于解决许多NP硬性问题。员工调度是许多组织面临的现实NP困难问题。在所有情况下进行自我安排并不总是实用的和可能的。护士名册与更早的法律转变中的高度有限的资源分配问题有关，该问题是使用不同的启发式算法解决的。在本文中，我们提出了基于GPGPU的PSO和ACO并行化来解决员工调度问题。为了使这两种算法并行，使用了主奴隶方法。 BCV 8.13.1数据集用于实验目的。结果分析是根据平均值，标准偏差，标准平均误差进行的。关键字：员工调度，并行化，PSO，GPGPU。
Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) are simple, easy to implement, their robustness to control parameters, and their computational efficiency when compared with mathematical algorithms and other heuristic optimization techniques. The calculation in PSO and ACO is very simple. Compared with the other developing calculations, it occupies the bigger optimization ability and it can be completed easily. It is used to solve many NP-Hard problems. Employee Scheduling is a real-life NP-Hard problem faced by many organizations. Self-scheduling in all situations is not always practical and possible. Nurse Rostering is related to highly constrained resource allocation problem into slots in a legal shift Earlier the problem was solved using different heuristic algorithms. In this dissertation, we have proposed, GPGPU based parallelization of PSO and ACO to solve Employee scheduling problems. To parallelize both algorithms, a master-slave approach is used. The BCV 8.13.1 data set is used for experimentation purposes. Analysis of results is done based on mean, standard deviation, standard mean error. Keywords: Employee Scheduling, Parallelization, PSO, GPGPU.
