A Multi-Aggregator Based Charge Scheduling in Internet of Electric Vehicles Using Fractional African Vulture Sail Fish Optimization

Abstract

Due to increase in pollution and greenhouse gas emissions from Internal Combustion Engines (ICEs), researchers and governments are looking forward for pollution-free, eco-friendly and efficient vehicle called Electric Vehicles (EVs), which is a best alternative for fossil fuel-based transportation systems. Limited battery capacity and frequent charging are essential for EVs. There are unscheduled EVs existed owing to the scarcity of energy and charging plugs. This research designs a multi aggregator-based charge scheduling strategy in Internet of Electric Vehicles (IoEVs) using proposed Fractional African Vulture Sail Fish Optimization (FrAVSO) algorithm. The FrAVSO is obtained by integrating Fractional Calculus (FC) with African Vulture Optimization (AVO) and Sail Fish Optimization (SFO). The energy is predicted utilizing Deep Maxout Network (DMN) and path selection is accomplished utilizing Fractional African Vulture Optimization (FrAVO). The FrAVSO achieved minimum distance of 15.313 Km, maximum energy of 0.252 J, minimum waiting time of 2.546 sec, and maximum profit of 94.978%.