A novel hybrid algorithm of imperialist competitive algorithm and teaching learning algorithm for optimal power flow problem with non-smooth cost functions

One of the major tools for power system operators is optimal power flow (OPF) which is an important tool in both planning and operating stages, designed to optimize a certain objective over power network variables under certain constraints. Without doubt one of the simple but powerful optimization algorithms in the field of evolutionary optimization is imperialist competitive algorithm (ICA); outperforming many of the already existing stochastic and direct search global optimization techniques. The original ICA method often converges to local optima. In order to avoid this shortcoming, we propose a new method that profits from teaching learning algorithm (TLA) to improve local search near the global best and a series of modifications is purposed to the assimilation policy rule of ICA in order to further enhance algorithm’s rate of convergence for achieving a better solution quality. This paper investigates the possibility of using recently emerged evolutionary-based approach as a solution for the OPF problem which is based on hybrid modified ICA (MICA) and TLA (MICA–TLA) for optimal settings of OPF control variables. The performance of this approach is studied and evaluated on the standard IEEE 30-bus and IEEE 57-bus test systems with different objective functions and is compared to methods reported in the literature. The hybrid MICA–TLA provides better results compared to the original ICA, TLA, MICA, and other methods reported in the literature as demonstrated by simulation results.