Hybrid differential evolution and Cuckoo search algorithm for solving economic dispatch problems

Economic dispatch (ED) is an important measure for energy conservation and consumption reduction in power systems, and is a typical multi constraint nonlinear optimization problem in mathematics. To address the limitations of traditional ED solving methods, a hybrid differential evolution (DE) and cuckoo search (CS) algorithm (HCS) is proposed. The HCS algorithm integrates the mutation and crossover operations of DE into the CS framework, enhancing population diversity and global search capability. Further refine the mutation parameters and operations to improve search efficiency and avoid premature convergence. Extensive simulations were conducted on three different ED scenarios, including 13, 40, and 15 unit systems, considering valve point effects, prohibited operating zones, and ramp rate limitations. The comparison results show that the HCS algorithm outperforms several recently published optimization techniques in terms of optimization accuracy, achieving a minimum cost of 17963.83 for 13 units and 121414.19 for 40 units. The HCS algorithm also demonstrates excellent convergence speed and stability, reflected in faster convergence curves and lower variance values. The proposed HCS algorithm combines the advantages of DE and CS, effectively solving the ED problem. Compared with other algorithms, it has advantages in optimization accuracy, convergence speed, and robustness.