Application of imperialist competitive algorithm with its modified techniques for multi-objective optimal power flow problem: A comparative study

One of the major tools for power system operators is Optimal Power Flow (OPF) problem which is designed to optimize a certain objective over power network variables under certain constraints. One of the simplest but most powerful optimization algorithms is imperialist competitive algorithm (ICA) outperforming many of the already existing optimization techniques. The original ICA method often converges to local optima. Therefore, in order to avoid this shortcoming, the interaction effects of colonies on each other are modeled to improve local search near the global optima. Also, a series of modifications is purposed to the assimilation policy rule of ICA method in order to further enhance algorithm’s rate of convergence for achieving a better solution quality. This article investigates the possibility of using recently emerged evolutionary-based approach as a solution for the OPF problems which is based on ICA method with its modified techniques for optimal settings of OPF control variables. The performance of this approach is studied and evaluated on the standard IEEE 57-bus test system with different objective functions and is compared to methods reported in the literature recently. The proposed modified techniques for ICA method provide better results compared to the original ICA and other methods recently reported in the literature as demonstrated by simulation results.