Most of world’s electrical energy demand is fulfilled by natural resources (Oil, Coal, Gas, etc.) and there a huge gap between supply. Therefore, utilities are facing the problem peak load burden. Broadly speaking, to sustain future electricity renewable/alternate sources (solar, wind must be integrated with smart grid (SG) cope demand. These freely available, inexhaustible can used as an alter...

In this paper, a variant of Grey Wolf Optimizer (GWO) that uses reinforcement learning principles combined with neural networks to enhance the performance is proposed. The aim is to overcome, by reinforced learning, the common challenges of setting the right parameters for the algorithm. In GWO, a single parameter is used to control the exploration/exploitation rate which influences the perform...

The dynamic video frame dataset’s automated feature analysis addresses the complexity of intensity mapping with normal and abnormal classes. Iterative modelling is needed to learn component a in several patterns for various data types threshold-based clustering analysis. GWO optimises Convoluted Pattern Wavelet Transform (CPWT) vectors employed this paper's CNN technique. A median filter reduce...

Optimal placement of Charging stations (CSs) and infrastructure planning are one the most critical challenges that face Electric Vehicles (EV) industry nowadays. A variety approaches have been proposed to address problem demand uncertainty versus optimal number CSs required build EV infrastructure. In this paper, a Markov-chain network model is designed study estimated on CS by using birth deat...

A modified variant of gray wolf optimization algorithm, namely, mean gray wolf optimization algorithm has been developed by modifying the position update (encircling behavior) equations of gray wolf optimization algorithm. The proposed variant has been tested on 23 standard benchmark well-known test functions (unimodal, multimodal, and fixed-dimension multimodal), and the performance of modifie...