Self-adaptive Differential Evolution Using Chaotic Local Search for Solving Power Economic Dispatch with Nonsmooth Fuel Cost Function

Abstract

The differential evolution (DE), proposed by Storn and Price, is a powerful population-based algorithm of evolutionary computation field designed for solving global optimization problems. The advantages of DE are its simple structure, easy use, convergence speed and robustness. However, the control parameters and learning strategies involved in DE are highly dependent on the problems under consideration. Choosing suitable parameter values requires also previous experience of the user. Despite its crucial importance, there is no consistent methodology for determining the control parameters of DE. In this chapter, different differential evolution approaches with self-adaptive mutation factor combined with a chaotic local search technique are proposed as alternative methods to solve the economic load dispatch problem of thermal units with valve-point effect. DE is used to produce good potential solutions, and the chaotic local search is used to fine-tune the DE run. DE and its variants with chaotic local search are validated for a test system consisting of 13 thermal units whose nonsmooth fuel cost function takes into account the valve-point loading effects. Numerical results indicate that performance of DE with chaotic local search presents best results when compared with previous optimization approaches in solving the load dispatch problem with the valve-point effect.