Relativity Pheromone Updating Strategy in Ant Colony Optimization for Constrained Unit Commitment Problem

This paper presents a development of the enhanced ant colony optimization (EACO) based on a novel approach of the relativity pheromone updating strategy (RPUS) for solving constrained unit commitment problem which cooperates with the candidate path management module (CPMM) embedded the effective repairing heuristic module (ERHM) in reducing search space and recovering a feasible optimality region so that a high quality solution can be acquired in a very early iterative. The adoption of RPUS not only enhances the search convergence of EACO, but also provides relatively pheromone information that is suitably exploited for a good guidance of search process. The EACO algorithms have been performed on a test system up to 100 generating units with a scheduling time horizon of 24 hours. The numerical results show better economical saving in the total operating cost when compared to the previous literature results. Furthermore, the proposed EACO topology can remarkably speed up the computation time of ant colony optimization algorithms, which is favorable for a large-scale UC problem implementation.