Solving software project scheduling problem using grey wolf optimization

In this paper, we will explore the application of grey wolf optimization (GWO) methodology in order to solve the software project scheduling problem (SPSP) to seek an optimum solution via applying different instances from two datasets. We will focus on the effects of the quantity of employees as well as the number of tasks which will be accomplished. We concluded that increasing employee number will decrease the project’s duration, but we could not find any explanation for the cost values for all instances that studied. Also, we concluded that, when increasing the number of the tasks, both the cost and duration will be increased. The results will compare with a max-min ant system hyper cube framework (MMAS-HC), intelligent water drops algorithm (IWD), firefly algorithm (FA), ant colony optimization (ACO), intelligent water drop algorithm standard version (IWDSTD), and intelligent water drop autonomous search (IWDAS). According to these study and comparisons, we would like to say that GWO algorithm is a better optimizing tool for all instances, except one instance that FA is outperform the GWO

A New K means Grey Wolf Algorithm for Engineering Problems

Purpose: The development of metaheuristic algorithms has increased by researchers to use them extensively in the field of business, science, and engineering. One of the common metaheuristic optimization algorithms is called Grey Wolf Optimization (GWO). The algorithm works based on imitation of the wolves' searching and the process of attacking grey wolves. The main purpose of this paper to overcome the GWO problem which is trapping into local optima. Design or Methodology or Approach: In this paper, the K-means clustering algorithm is used to enhance the performance of the original Grey Wolf Optimization by dividing the population into different parts. The proposed algorithm is called K-means clustering Grey Wolf Optimization (KMGWO). Findings: Results illustrate the efficiency of KMGWO is superior to GWO. To evaluate the performance of the KMGWO, KMGWO applied to solve 10 CEC2019 benchmark test functions. Results prove that KMGWO is better compared to GWO. KMGWO is also compared to Cat Swarm Optimization (CSO), Whale Optimization Algorithm-Bat Algorithm (WOA-BAT), and WOA, so, KMGWO achieves the first rank in terms of performance. Statistical results proved that KMGWO achieved a higher significant value compared to the compared algorithms. Also, the KMGWO is used to solve a pressure vessel design problem and it has outperformed results. Originality/value: Results prove that KMGWO is superior to GWO. KMGWO is also compared to cat swarm optimization (CSO), whale optimization algorithm-bat algorithm (WOA-BAT), WOA, and GWO so KMGWO achieved the first rank in terms of performance. Also, the KMGWO is used to solve a classical engineering problem and it is superiorComment: 15 pages. World Journal of Engineering, 202

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

Optimal Coordinated Control of DC Microgrid Based on Hybrid PSO–GWO Algorithm

Microgrids (MGs) are capable of playing an important role in the future of intelligent energy systems. This can be achieved by allowing the effective and seamless integration of distributed energy resources (DERs) loads, besides energy-storage systems (ESS) in the local area, so they are gaining attraction worldwide. In this regard, a DC MG is an economical, flexible, and dependable solution requiring a trustworthy control structure such as a hierarchical control strategy to be appropriately coordinated and used to electrify remote areas. Two control layers are involved in the hierarchy control strategy, including local- and global-control levels. However, this research focuses mainly on the issues of DC MG’s local control layer under various load interruptions and power-production fluctuations, including inaccurate power-sharing among sources and unregulated DC-bus voltage of the microgrid, along with a high ripple of battery current. Therefore, this work suggests developing local control levels for the DC MG based on the hybrid particle swarm optimization/grey wolf optimizer (HPSO–GWO) algorithm to address these problems. The key results of the simulation studies reveal that the proposed control scheme has achieved significant improvement in terms of voltage adjustment and power distribution between photovoltaic (PV) and battery technologies accompanied by a supercapacitor, in comparison to the existing control scheme. Moreover, the settling time and overshoot/undershoot are minimized despite the tremendous load and generation variations, which proves the proposed method’s efficiency

Optimización de la regresión de mínimos cuadrados parciales con funciones Kernel

La regresión de mínimos cuadrados parciales (PLS) es un método lineal que busca predecir un conjunto de variables dependientes a partir de un conjunto de predictores, extrayendo factores ortogonales que maximizan la capacidad predictiva, también llamados componentes. Cuando las estructuras de datos exhiben variaciones no lineales, se recurre a la regresión de mínimos cuadrados parciales con kernel (KPLS), que transforma los conjuntos de datos originales a un espacio de características de dimensionalidad arbitraria donde sea posible la generación de un modelo lineal. Una dificultad recurrente al implementar la regresión KPLS es determinar el número de componentes y los parámetros de la función kernel que maximizan su desempeño.