Particle swarm optimization-based approach for generator maintenance scheduling

This paper introduces a particle swarm optimization-based method for solving a multi-objective generator maintenance scheduling problem with many constraints. It is shown that the particle swarm optimization-based approach is effective in obtaining feasible schedules in a reasonable time. Actual data from a practical power system was used in this study and results were compared against those from other evolutionary methods on the same set of data. This paper also introduces a novel concept for the spawning and selection mechanism in a hybrid particle swarm algorithm. The results suggest that this hybrid model converges to a better solution faster than the standard PSO algorithm. It is envisaged that this hybrid approach can be easily implemented for similar optimization and scheduling problems to obtain better convergence.