Solving unit commitment (UC) problem is one of the most critical tasks in electric power system
operations. Therefore, proposing an accurate method to solve this problem is of great interest. The
original bacterial foraging (BF) optimization algorithm suffers from poor convergence
characteristics for larger constrained optimization problems. In addition, the stopping criterion
used in the original BF increases the computation burden of the original algorithm in many cases.
To overcome these drawbacks, a hybridized adaptive bacterial foraging and genetic algorithm
(HABFGA) approach is proposed in this paper to solve the unit commitment problem with ramp
rate constraint. The HABFGA approach can be derived by combining adaptive stopping criterion,
BF algorithm and genetic algorithm, so that the drawbacks of original BF algorithm can be treated
before employing it to solve the complex UC problem. To illustrate the effectiveness of the
HABFGA approach, several standard and real test systems with different numbers of generating
units are used. The results of HABFGA approach are compared with the results obtained using
other published methods employing same test systems. This comparison shows the effectiveness
and the superiority of the proposed method over other published methods.