Hybrid differential evolution with biogeography-based optimization algorithm for solution of economic emission load dispatch problems

This paper presents combination of differential evolution (DE) and biogeography-based optimization (BBO) algorithm to solve complex economic emission load dispatch (EELD) problems of thermal generators of power systems. Emission substances like NOX, SOX, COX, Power demand equality constraint and operating limit constraint are considered here. Differential evolution (DE) is one of the very fast and robust, accurate evolutionary algorithms for global optimization and solution of EELD problems. Biogeography-based optimization (BBO) is another new biogeography inspired algorithm. Biogeography deals with the geographical distribution of different biological species. This algorithm searches for the global optimum mainly through two steps: migration and mutation. In this paper combination of DE and BBO (DE/BBO) is proposed to accelerate the convergence speed of both the algorithm and to improve solution quality. To show the advantages of the proposed algorithm, it has been applied for solving multi-objective EELD problems in a 3-generator system with NOX and SOX emission, in a 6-generators system considering NOX emission, in a 6-generator system addressing both valve-point loading and NOX emission. The current proposal is found better in terms of quality of the compromising and individual solution obtained.

► Combination of differential evolution and biogeography-based optimization presented.
► Solved economic emission load dispatch problems with NOX, SOX, COX emission.
► Combination of DE and BBO accelerate the convergence speed of the algorithm.
► Solved EELD problems in 3,6 generator system with and without valve-point loading.
► The proposal is better in terms of the compromising and individual solution.