An evolutionary programming based neuro-fuzzy technique for multiobjective generation dispatch

An integrated approach combining an evolutionary programming based fuzzy coordination and an artificial neural network methods along with a heuristic rule based search algorithm has been developed in this paper in order to obtain the best compromising optimal generation schedules for multiobjective generation dispatch problem with non-smooth characteristic functions satisfying various practical constraints. Initially, the economy objective function is minimized, followed by minimization of emission level objective function. Then, both the objectives are combined through a fuzzy coordination method to form a fuzzy decision making (FDM) function. Maximizing the FDM function then solves the original two-objective problem. The minimization and maximization tasks of this optimization problem are solved by the evolutionary programming technique and the results are trained by a radial basis function ANN to reach a preliminary generation schedule. Since, some practical constraints may be violated in the preliminary schedule, a heuristic rule based search algorithm is developed to reach a feasible best compromising generation schedule which satisfies all practical constraints in the final stage. The proposed EP based neuro-fuzzy technique has been applied to IEEE-30 bus test system and the results are presented.