On the efficiency of metaheuristics for solving the optimal power flow

In this article, various nonlinear and non-convex optimal power flow (OPF) objective functions are optimized using eight different optimization algorithms, i.e., moth-flame optimization algorithm (MFO), gray wolf optimizer (GWO), dragonfly algorithm (DA), sine-cosine algorithm (SCA), antlion optimizer (ALO), multi-verse optimizer (MVO), grasshopper algorithm (GOA) and ion motion algorithm (IMO) for different test systems, i.e., IEEE 57-bus test system and IEEE 118-bus test system. As per no free lunch algorithm, in general, no algorithm can be considered better than other, and hence, the performance of algorithm relies heavily upon system under consideration. Applications through 22 different case studies are pursued to assess efficiency of algorithms under consideration. An attempt has been made to evaluate algorithms based on the best solution, average solution, average simulation time and trend of convergence. Different statistical tests are performed to identify the efficiency of algorithms under consideration. Test results suggest that MFO performs better as compared to rest of the algorithms for most test cases proving its mettle in dealing with nonlinear and non-convex OPF problem.