Fractional Jaya election-based optimization enabled routing and charge scheduling for electric vehicle in cloud-assisted Vehicular Ad Hoc NETwork

Electric vehicles (EVs) are the emerging environmentally friendly approach that is used to minimize greenhouse gases and carbon dioxide (CO₂) emissions in the atmosphere. A clear strategy is required for scheduling charging stations (CS) to EVs based on their applications. In this research work, a robust routing and effective charge scheduling approach are devised using a cloud-assisted Vehicular Ad Hoc NETwork (VANET) for charging EVs. Here, the multi-objectives, like predicted traffic density, battery power, and distance, are used to identify the optimal routing of EV to CS. The predicted traffic density is evaluated using Deep Long Short Term Memory (DLSTM) and is trained using a developed Jaya Election-Based Optimization Algorithm (JEBOA), which is the incorporation of Jaya Optimization (Jaya) and Election-Based Optimization Algorithm (EBOA). Next to optimal routing, the charge scheduling process is carried out using the Fractional Jaya Election-Based Optimization Algorithm (Fractional JEBOA) by considering the priority, response time, and latency of the EV. The designed Fractional JEBOA is the integration of Fractional Calculus (FC) and the developed JEBOA. Moreover, the various evaluation metrics are considered to calculate the performance of the designed method, which attained a delay of 0.243 ms, distance of 35 km, power of 95 W, response time of 0.441 s and traffic density of 0.664.