gray wolf optimization algorithm

In today’s competitive world, companies need to analyze, identify and predict the behaviorof their customers and respond to their demands earlier than their competitors. Moreover, in manyindustries such as mobile telecommunications, the cost of maintaining existingcustomers (customer retention)is much lower than the cost of attracting a new customer. Therefore,the problem of identifying customers who are going to leave the company, so-called Customer Churn Prediction (CCP),and preventing themby offering Incentivesis essential in these industries. In this direction, researchers have presentedefficient methods using data mining and artificial intelligence tools to identify potentialchurners. In order to improve the process of predicting customer churn, in this paper we propose a novelensemble learningbased approach, in which the Gray Wolf Optimization(GWO) algorithm is utilized to select the effective features and also adjust the hyper-parameters in the proposed model. We have implemented our proposed model using Python and simulated it on the IBM Telco dataset to evaluate itsperformanceand compared the obtained results with existingmethods using common evaluation criteria including accuracy, precision, recall,and AUC. The experimentalresults show the superiority of the proposed method over other evaluated solutions.

Reactive power has countless benefits for power systems, which are called reactive power sub-services. One of these services is voltage control. The main goals in this research are to find the suitable price for reactive power and set the voltage of the buses in the allowed range and at the same time consider the suitable amount of reactive power in the generation buses as a reserve for technical and voltage support in the power system. In order to optimize the objective functions, the gray wolf optimization algorithm has been used and 33 bus radial distribution system has been considered and it has been assumed that the load of the power system changes during 24 hours and it is assumed that in this distribution network, generators are placed in some buses. The simulation results show that in addition to the cost of reactive power being minimized, a suitable amount of reactive power is also stored in the generation buses and the voltage deviations of the buses are also minimized.