Enhancing The Route Optimization Using Hybrid Optimization Algorithm For The Internet Of Vehicle's

Abstract

The rapid advancement of the Internet of Things (IoT) has catalyzed the integration of connected vehicles into smart logistics, reshaping the transportation landscape. This research explores the development of a distributed intelligent traffic system by endowing connected vehicles with decision-making capabilities to navigate intricate traffic scenarios like roundabouts and intersections. Proposing a model for the next-generation Intelligent Transportation System (ITS), the study emphasizes dynamic decision-making rooted in ant colony optimization, a cornerstone algorithm in Swarm Intelligence (SI). A communication framework facilitates the exchange of traffic flow information among connected vehicles, while SI principles treat these vehicles as artificial ants, enabling adaptive decision-making in real-time traffic dynamics. Furthermore, the research introduces an effective order-aware hybrid genetic algorithm for the capacitated vehicle routing problem in the IoT context, characterized by an improved initialization strategy and a problem-specific crossover operator. Simulations validate the efficacy of the proposed approach in optimizing routing for capacitated vehicles within smart logistics networks.