Evolutionary programming techniques for economic load dispatch

TLDR

Evolutionary programming is a versatile optimization tool for nonlinear problems, yet its real‑world application to economic load dispatch remains largely unexplored. This study evaluates the performance of evolutionary programs on economic load dispatch problems in two parts. Part I introduces scaled‑cost adaptation modifications, while Part II develops programs that adapt using an empirical learning rate. The algorithms’ absolute and relative performance were assessed on ELD problems of varying size and complexity with nonconvex cost curves, where conventional gradient methods fail.

Abstract

Evolutionary programming has emerged as a useful optimization tool for handling nonlinear programming problems. Various modifications to the basic method have been proposed with a view to enhance speed and robustness and these have been applied successfully on some benchmark mathematical problems. But few applications have been reported on real-world problems such as economic load dispatch (ELD). The performance of evolutionary programs on ELD problems is examined and presented in this paper in two parts. In Part I, modifications to the basic technique are proposed, where adaptation is based on scaled cost. In Part II, evolutionary programs are developed with adaptation based on an empirical learning rate. Absolute, as well as relative, performance of the algorithms are investigated on ELD problems of different size and complexity having nonconvex cost curves where conventional gradient-based methods are inapplicable.