Elephant Herding Optimization for Solving Non-convex Optimal Power Flow Problem

Abstract

The optimal power flow (OPF) is becoming the most popular problem encountered in studies related to power system analysis. OPF is formulated as a nonlinear optimization problem with conflicting objectives and subjected to both equality and inequality constraints. In this paper, inspired by the herding behavior of elephant, a new type of swarm-based metaheuristic search method, called Elephant Herding Optimization (EHO), is proposed for solving OPF problem. EHO has a fast convergence rate due to the use of clan updating operator and separating operator. The elitism scheme is also used to save the best elephant during the process when updating the elephant. Case studies based on standard IEEE 30-bus test system are employed to prove the capability of the proposed EHO algorithm. The results clearly show the superiority of EHO in searching for the better function values than other well-known metaheuristic search algorithms that has been already done.