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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.
Date of Conference: 24-26 April 2003