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This paper presents a new approach for formulating the unit commitment problem which results in a considerable reduction in the number of decision variables. The scheduling variables are coded as integers representing the operation periods of a generating unit. The unit commitment problem is solved using a new parameter free adaptive particle swarm optimization (APSO) approach. This algorithm provides solutions to the major demerits of PSO such as parameter tuning, selection of optimal swarm size and problem dependent penalty functions. The constrained optimization problem is solved using adaptive penalty function approach. The penalty terms adapt to the performance of the swarm. So no additional penalty coefficient tuning is required. This paper describes the proposed algorithm with the new variable formulation and presents test results on a ten unit test system. The results demonstrate the robustness of the new algorithm in solving the unit commitment problem.