  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.
