Shuffled differential evolution for large scale economic dispatch

In this paper, a novel metaheuristic optimization methodology is proposed to solve large scale nonconvex economic dispatch problem. The proposed approach is based on a hybrid shuffled differential evolution (SDE) algorithm which combines the benefits of shuffled frog leaping algorithm and differential evolution. The proposed algorithm integrates a novel differential mutation operator specifically designed to effectively address the problem under study. In order to validate the SDE methodology, detailed simulation results obtained on three standard test systems 13, 40, and 140-unit test system are presented and discussed. Transmission losses are considered along with valve point loading effects for 13 and 40-unit test systems and calculated using B-coefficient matrix. A comparative analysis with other settled nature-inspired solution algorithms demonstrates the superior performance of the proposed methodology in terms of both solution accuracy and convergence performances.

► We propose shuffled differential evolution which integrates shuffled frog leaping algorithm and differential evolution.
► SDE method succeeded in finding best solution for 13, and 40-unit systems with and without considering transmission losses.
► The feasibility of SDE has been investigated on large scale test system considering 140-units with valve point effects.
► SDE method can greatly enhance the searching ability, and also efficiently manages the system constraints.