Task Scheduling with Altered Grey Wolf Optimization (AGWO) in Mobile Cloud Computing using Cloudlet

Mobile devices can improve their battery life by offloading their tasks to a nearby cloudlet instead of executing tasks on the mobile device. Because mobile devices have low-speed processors, small-size memory, and limited battery. As the mobile devices are moving, they are connected and disconnected from the cloudlets. So, their tasks are offloaded to the new cloudlets and also migrated from one cloudlet to another until the tasks finish their execution. Scheduling these tasks in the cloudlet will reduce the tasks\u27 execution time and the mobile device\u27s power consumption using this proposed new method (AGWO). The GWO algorithm is modified to accept the inputs from a two-dimensional array instead of sequence inputs and search for the prey within the two-dimensional array instead of an unknown circle area. This method deals with the arrival time of the task, task size, and big task. The migration of the partially executed task dynamically to other VMs is also examined. This proposed method also reduces the average scheduling delay and increases the percentage of requests executed by the cloudlet than other variations of GWO and other research algorithms

An Improved Artificial Intelligence Based on Gray Wolf Optimization and Cultural Algorithm to Predict Demand for Dairy Products: A Case Study

This paper provides an integrated framework based on statistical tests, time series neural network and improved multi-layer perceptron neural network (MLP) with novel meta-heuristic algorithms in order to obtain best prediction of dairy product demand (DPD) in Iran. At first, a series of economic and social indicators that seemed to be effective in the demand for dairy products is identified. Then, the ineffective indices are eliminated by using Pearson correlation coefficient, and statistically significant variables are determined. Then, MLP is improved with the help of novel meta-heuristic algorithms such as gray wolf optimization and cultural algorithm. The designed hybrid method is used to predict the DPD in Iran by using data from 2013 to 2017. The results show that the MLP offers 71.9% of the coefficient of determination, which is better compared to the other two methods if no improvement is achieved