Optimal power flow using self-learning cuckoo search algorithm

This paper proposes an improved Cuckoo search algorithm to solve optimal power flow problems in electric power system. The proposed Self-learning Cuckoo search algorithm enhances the performance of Cuckoo eggs by employing the learner phase of Teaching-learning-based optimization. The learner phase leads Cuckoo eggs to follow better solutions. The proposed method has been applied for solving optimal power flow problems on the standard IEEE 30-bus and 57-bus systems to investigate its effectiveness. The objective of the optimal power flow problem is to minimize the total fuel cost while satisfying generator operational constraints of generators, transformers, shunt capacitors and capacity of transmission lines. The results indicate that the proposed method gives better solutions than the conventional Cuckoo search algorithm and other algorithms in literature.