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

• Authors: Mojtaba Ghasemi;Sahand Ghavidel;Shima Rahmani;Alireza Roosta;Hasan Falah
• Venue: Engineering Applications of Artificial Intelligence
• Year: 2014

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.