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This paper proposes a variable-dimension optimization approach to address the high dimensionality issues in solving the unit commitment problem. This method introduces the concept of adaptive search space dimension. The proposed approach is implemented in particle swarm optimization algorithm. The optimization process starts with an arbitrary problem dimension, adapts with respect to the swarm progress and finally selects the optimal dimensional space. The efficiency of this method is tested on a ten-unit test system. The results are compared with binary programming and fixed duty cycle approaches. The simulation results show that the proposed method results in considerable reduction of problem dimension, faster convergence and improved quality of the final solution.