gray wolf algorithm

Fog computing is increasingly used as a platform for processing Internet of Things applications. Thus, this architecture extends cloud computing services to the edge of the network, where processing may be cheaper and faster. One of the main challenges in providing Quality of Service (QoS) requirements, such as delay and energy consumption in the fog environment, is to pay attention to the energy limitation and low computational capacity of fog nodes, which makes it difficult to assign tasks to fog nodes. This paper first presents a mathematical model for resource allocation with the aim of reducing delay and energy while considering QoS criteria. Then, a combined genetic and grey wolf algorithm is introduced to solve the model. Note that the combination of these two algorithms leads to finding an optimal solution efficiently. Although the implementation of the proposed algorithms has processing costs and computational delay, due to the improvement of QoS criteria, this cost can be ignored. The results show that the combination and simultaneous use of the positive points of the two algorithms improves execution time and completion time of the last task, as well as energy consumption by 18.30%, 15.14%, and 10.21%, respectively, compared to the semi-greedy method.

One of the biggest problems facing the human being is energy supply due to reduced resource and cost. The largest share of energy consumption in the world has been allocated to the construction sector. The main sources of energy supply are coal, natural gas and oil, all of which are non-renewable and will be completed in the near future. Major energy consumers can be referred to household, industrial, agricultural, general, commercial, and street lighting. Among the energy consumers, the share of domestic and office sectors is higher than other consumers, and attention to reducing energy consumption and energy losses in the construction sector is an unavoidable necessity. In this paper, a new method of building energy management is proposed, which, with the help of internet networks of objects, controls the energy consumption of buildings. An administrative building with six areas is considered. The proposed method consists of two phases: the first phase, which is the prediction stage, is performed using artificial neural network and six parameters: outside temperature of the building, set point temperature, sun radiation, occupancy, previous temperature and the hour of the day are given as inputs to the perceptron neural network and the output of this phase is inside temperature of building and the energy consumption, which is given as input to the next phase. The second phase uses the gray wolf algorithm to determine the optimal temperature for each part of the building at any hour of the day. The energy consumption and cost of the building are calculated using the software of MATLAB, which results in a significant reduction in energy consumption and energy cost optimization in the office. The proposed method shows a reduction in energy consumption of 22 Kw/h in the early morning hours.

Nowadays, permanent magnet synchronous motors have been widely used in industry due to the elimination of excitation losses, longer life and higher efficiency. Errors in engine and drive systems are unavoidable during operation. Therefore, a suitable scenario should be considered for when these systems fail. If the necessary predictions and control algorithms are not considered for the error conditions, heavy damages will be imposed on equipment and devices, especially at high power levels. One of the common control methods in inverters and synchronous motor drives is the proportional-integral control method. In most cases, simple proportional-integral control with fixed interest rates is used. In this paper, fuzzy logic with suitable membership functions is used to adaptively and intelligently determine the benefits of proportional-integral controller to control the speed of the permanent magnet synchronous motor in the event of an error. By combining fuzzy logic and gray wolf algorithm to achieve the optimal controller, a method is presented that takes advantage of the fuzzy controller along with the proportional-integral controller and using the gray wolf algorithm for optimization. Reconstruction results in optimal and stable control under various fault conditions. The proportional-integral controller interests used in conjunction with the gray wolf algorithm for speed control are determined using fuzzy logic for error conditions. In this paper, the proposed method in MATLAB software Simulink environment is simulated in two stages, once for step changes of reference speed and torque and once under three types of errors, and the results show improved reference speed tracking and reduction Distortion of three-phase currents and torque fluctuations.

The electricity market in the world today is scientifically recognized and it is becoming more competitive every day. In the meantime, the price forecasting tool helps market participants gain more profit. The purpose of the present study is to extend the neural network and optimize it by gray wolf algorithm to predict electricity market price. Since traditional and neural network models have always used probabilistic methods to increase prediction accuracy, this model has tried to ignore these by introducing a new method to require less time for prediction. One of the issues that always affects the prediction accuracy is the existence of critical and sudden cases in the system, which will be controlled in this research using efficient methods. The proposed model uses Nordic electricity market information, but it is obviously applicable to any other market. Finally, to illustrate the accuracy of this model, a comparison is made between it and one of the traditional methods.