Genetic evolving ant direction PSODV hybrid algorithm for OPF with non-smooth cost functions

In this paper, a novel method called evolving ant direction particle swarm optimization with differentially perturbed velocity (EADPSODV) hybrid algorithm has been presented to solve the optimal power flow problem with non-smooth and non-convex generator fuel cost characteristics. In this approach, ant colony search is utilized by the EADPSODV algorithm to find a suitable mutation operator for particle swarm optimization with differentially perturbed velocity (PSODV). Genetic algorithm method is employed to evolve the ant colony parameters. The power flow problem is solved by the Newton–Raphson method. The performance of the proposed approach has been demonstrated on IEEE 30-bus and IEEE 118-bus test systems with three different objective functions. Investigations reveal that the EADPSODV provides significantly better results compared to classical PSODV and other methods reported in the literature recently.