Optimal Path Selection in Vehicular Adhoc Network Using Hybrid Optimization

Abstract

Businesses, academics, and professionals are concentrating on enhancing safety as a consequence of the swift development of wireless technology. The gathering of meaningful traffic data enables travelers to choose more informed routes. The main query is, “How do we receive traffic information? Although it is frequently necessary to conduct further research on issues like "How can we collect traffic information efficiently and economically?" and "Which path is the shortest trip time between two vertices?”. The goal of this research project is to identify the best, quickest route between the starting point and the finishing point. This is accomplished by the proposed BHGWO model. This paper intends to introduce an optimal path or route selection in VANET. Between the source and the destination, the vehicle information should be noted for the selection of better paths or routes. From this, the mobility and congestion details are stored in the IoT. In prior, the possible paths are listed out, from which the optimal route or path is selected. The Bat Hybridized Grey Wolf Optimization (BHGWO) method, a new hybrid optimization model that conceptually combines the Bat Optimization Algorithm (BOA) with the Grey Wolf Optimization (GWO) algorithm, is presented as a solution to this problem. The accepted BHGWO model's convergence analysis was calculated using the conventional schemes ACO, GA, GWO, CSA, and BOA, correspondingly. Further, the performance was calculated with respect to congestion, delay, and energy based on 50 vehicles, 100 vehicles, 150 vehicles, and 200 vehicles, respectively.