A hybrid genetic algorithm and bacterial foraging approach for dynamic economic dispatch problem

• A novel hybrid algorithm (HGABF) using GA and BF algorithms is introduced.
• The HGABF is applied to solve the non-convex dynamic economic dispatch problem.
• The presented method has been evaluated using three standard test systems.
• The worst cost obtained by HGABF is better than the best values of other methods.
• Results show the effectiveness and the superiority of HGABF over other methods.

Dynamic economic dispatch (DED) is one of the most significant non-linear complicated problems showing non-convex characteristic in power systems. This is due to the effect of valve-points in the generating units’ cost functions, the ramp-rate limits and transmission losses. Hence, proposing an effective solution method for this optimization problem is of great interest. The original bacterial foraging (BF) optimization algorithm suffers from poor convergence characteristics for larger constrained problems. To overcome this drawback, a hybrid genetic algorithm and bacterial foraging (HGABF) approach is presented in this paper to solve the dynamic economic dispatch problem considering valve-point effects, ramp-rate limits and transmission losses. The HGABF approach can be derived by integrating BF algorithm and genetic algorithm (GA), so that the BF’s drawback can be treated before employing it to solve the complex and high dimensioned search space of the DED problem. To illustrate the effectiveness of the HGABF approach, several test systems with different numbers of generating units are used. The results of HGABF approach are compared with those obtained by other published methods employing same test systems. These results show the effectiveness and the superiority of the introduced method over other published methods.