This paper presents a Differential Evolution algorithm combined with Opposition Based Learning (DE-OBL) to solve Economic Load Dispatch problem with non-smooth fuel cost curves considering transmission losses, power balance and capacity constraints. The proposed algorithm varies from the Standard Differential Evolution algorithm in terms of three basic factors. The initial population is generated through the concept of Opposition Based Learning, applies tournament based mutation and uses only one population set throughout the optimization process. The performance of the proposed algorithm is investigated and tested with two standard test systems, the IEEE 30 bus 6 unit system and the 20 unit system. The experiments showed that the searching ability and convergence rate of the proposed method is much better than the standard differential evolution. The results of the proposed approach were compared in terms of fuel cost, computational time, power loss and individual generator powers with existing differential evolution and other meta-heuristics in literature. The proposed method seems to be a promising approach for load dispatch problems based on the solution quality and the computational efficiency.
P. Surekha and Sumathi, S., “Solving Economic Load Dispatch problems using Differential Evolution with Opposition Based Learning”, WSEAS Transactions on Information Science and Applications, vol. 9, no. 1, pp. 1-13, 2012.