Using an enhanced genetic algorithm to solve the unit commitment problem

The genetic algorithm (GA) is a general purpose optimization technique based on mechanisms inspired from natural genetics and natural selection. It is very suitable for solving nonlinear, multi-constraint, combinatorial optimization problems which are tough for conventional methods, However, the simple genetic algorithm (SGA) may have a slow convergence or even cannot reach the global optimum. Therefore, an enhanced GA is proposed in this paper to solve the unit commitment problem in power systems. The new features of the enhanced GA include chromosome mapping, problem specific operators and local search technique. As expected, it has a significantly improved performance of finding the optimum solution to the unit commitment problem