Solution of Economic Power Dispatch Problems Using Oppositional Biogeography-based Optimization

This article describes a quasi-reflection oppositional biogeography-based optimization for the solution of complex economic load dispatch problems of thermal power plants. This algorithm can take care of economic load dispatch problems, considering different constraints such as transmission losses, ramp rate limits, valve-point loading, and prohibited operating zones. Biogeography deals with the geographical distribution of different biological species. Mathematical models of biogeography describe how a species arises, migrates from one habitat (island) to another, and disappears. This algorithm searches the global optimum mainly through two steps: migration and mutation. This article presents a quasi-reflection oppositional biogeography-based optimization to accelerate the convergence of biogeography-based optimization and to improve solution quality. The proposed method employs opposition-based learning along with a biogeography-based optimization algorithm. Instead of opposite numbers, here, quasi-reflected numbers are used for population initialization and also for generation jumping. The effectiveness of the proposed algorithm has been verified on four different test systems. Compared with the other existing techniques, the proposed algorithm has been found to perform better in a number of cases. Considering the quality of the solution and convergence speed obtained, this method seems to be a promising alternative approach for solving the economic load dispatch problems.