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This paper presents an efficient approach for solving the unit commitment problemusing an hybrid approach based on Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Unit Commitment Problem (UCP) used for achieving minimum operating cost in the scheduling operation of power system generating units subjected to needed demand and reserve constraints is a nonlinear mixed integer optimization problem. The on/off state of the generating units at each hour interval of the planning period that optimally transmits load and reserve among the committed units is determined by the UC problem. The significance of the need for a more competent optimal solution to the UCP problem is increasing with the routinely changing demand. To accomplish this, hereby, we propose a hybrid approach that yields optimal commitment of the units by solving the unit commitment problem subjected to essential constraints. To verify the effectiveness of the proposed method numerical studies have been performed for the NTPS 7 unit system for a period of 24 hours. The results show that the proposed method outperforms the other state-of-the-art algorithms and the conventional methods in solving the unit commitment problems. [ABSTRACT FROM AUTHOR]