An across neighborhood search algorithm with a solution difference perturbation mechanism for solving economic dispatch problems

Abstract

The economic dispatch problem is a kind of complex real-world non-convex optimization problem, which minimizes the total operating cost while being subject to a collection of complex equality and inequality constraints. This study presents a novel meta-heuristic named across neighborhood search (ANS) algorithm to address economic dispatch problems. ANS is augmented by a solution-difference disturbance mechanism. It is generally hard for meta-heuristics to handle complex nonlinear equality constraints, because a meta-heuristic's search behavior is essentially stochastic while the equality constraints require the algorithm to exactly locate feasible solutions in the constraint bound. Therefore, a variable reduction strategy (VRS) is employed to deal with equality constraints when solving economic dispatch problems. VRS eliminates the equality constraints and reduces the dimensionality of the problem simultaneously, such that significantly improves the optimization efficiency. Extensive experiments and comparisons indicate that the augmented ANS with VRS could generate promising results for economic dispatch problems.