基于GPGPU的ACO和PSO算法求解员工调度问题的比较分析
Comparative Analysis of GPGPU based ACO and PSO Algorithm for Employee Scheduling Problems
摘要：
粒子群优化算法（PSO）和蚁群优化算法（ACO）与数学算法和其他启发式优化技术相比，具有简单、易于实现、对控制参数具有鲁棒性、计算效率高等优点。PSO和ACO的计算非常简单。与其它发展计算相比，它具有优化能力大、计算简单等优点。它被用来解决许多NP-Hard问题。员工调度是许多组织面临的现实生活中的NP-Hard问题。在所有情况下，自我计划并不总是可行和可能的。护士排班是涉及到高度约束的资源分配问题到插槽的法律转变早些时候，这个问题是解决使用不同的启发式算法。在这篇论文中，我们提出了基于GPGPU的并行PSO和ACO解决员工调度问题。为了并行化这两种算法，使用主从方法。BCV 8.13.1数据集用于实验目的。根据平均值、标准差、标准平均误差对结果进行分析。关键词：员工排程，并行化，粒子群算法，GPGPU。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
