Gbest based Artificial Bee Colony optimization for unit commitment problem

Unit commitment (UC) is one of the most difficult optimal tasks of the power system. The main objective of this study is to solve UC problem to attain minimum operating cost while satisfying all the constraints over a period of time after proper scheduling of the generating units using three evolutionary techniques, namely Particle Swarm Optimization (PSO), Differential Evaluation (DE) and Gbest Artificial Bee Colony (GABC) algorithms. These algorithms are applied to 10 and 20 unit test system over a 24 hour scheduling period and the results are compared with the existing optimization method Intelligent Binary Particle Swarm Optimization (IBPSO) reported in literature. It is found that the results achieved by applying Gbest Artificial Bee Colony algorithm are better than other two proposed methods.