Optimal Power Flow Solution with Nature Inspired Antlion Meta-Heuristic Algorithm

Abstract

Currently, optimal power flow (OPF) problem is widely encountered issue. This has been formulated and programmed with the help of nature-inspired meta-heuristic approach known as Antlion Optimization algorithm (ALO) in this work. The algorithm is built upon the basic hunting procedure of antlions. The antlions hunting behavior is summarized into prominent set of steps like random walks, trap building to clasp ants, catching ants, and five pit reorganizations. This behavior is then exploited to resolve OPF problem. The ALO technique has shown higher convergence characteristics with the help of the Roulette Wheel selection. A typical system of IEEE 30 bus has been considered for validating the proposed method. Moreover, with suitable OPF formulation, numerous objectives could be solved. Some of which have been taken into account here. The results of ALO are studied and their effectiveness is compared with the OPF algorithms like Particle Swarm Optimization (PSO) method and Black Hole-Based Optimization (BHBO) method. The results demonstrate the usefulness of ALO with the improved convergence for OPF problem based on the population of 40 when compared to nature-inspired techniques like PSO and BHBO.