Oppositional Biogeography-Based Optimization for multi-objective Economic Emission Load Dispatch

This Paper presents an Oppositional Biogeography-Based Optimization algorithm to solve complex Economic Emission Load Dispatch (EELD) problems of thermal power systems. Emission of NO_x and SO_x are considered for case studies. The proposed method is a modification over Biogeography-Based Optimization technique, designed to accelerate its convergence rate and to improve the quality of solution. This method combines opposition-based learning scheme along-with migration concept of BBO. Instead of ordinary random numbers, quasi-reflected numbers have been employed in this work for initialization of population and also for a new operation, namely, generation jumping. The proposed algorithm has been applied for solving multi-objective EELD problems in a 3 Generator system with NO_X, SO_X emission and in a 6 Generator system with both valve-point loading and NO_X emission. The superiority of the proposed method over other alternatives has been demonstrated. Considering the solution quality the proposed method seems to be a promising alternative to solve these problems.