Dynamic Multi-Objective Optimization in Vehicular Fog Computing With NSGA-II+

Abstract

Vehicular Fog Computing (VFC) presents a promising paradigm to reduce latency and energy usage through utilization of nearby edge resources by vehicles. Yet, efficient and scalable resource management is still a significant challenge particularly due to dynamic network topologies, resource, and high Quality of Service (QoS) requirements. Traditional metaheuristic methods such as GA and PSO are limited in convergence speed and solution quality under such restrictions. This research introduces Enhanced NSGA-II+, a cutting-edge multi-objective evolutionary model enhancing NSGA-II and NSGA-III through dynamic population adaptation, Pareto-front-leveraged selection, and premature convergence prevention. Experimental comparisons in both common and ultra-dense vehicular settings with up to 1000 vehicles and 2000 tasks show that NSGA-II+ outperforms baseline algorithms by far, reducing average delay by 72.55% (compared to NSGA-II) and 71.75% (vs. NSGA-III), and energy cost by 70.96% (compared to NSGA-II) and 70.75% (compared to NSGA-III). This reinforces how NSGA-II+ addresses both dynamic topologies and resource heterogeneity. Its strong exploration-exploitation trade-off and flexibility render it an appealing solution for real-time, energy-efficient deployment in smart transportation systems.