Enhanced Harris Hawk Multi-Objective Optimization Algorithm for Cognitive Radio-Vehicular Ad Hoc Networks

In this paper is presented an Enhanced Harris Hawk Multi-Objective Optimization (EH-HHO) algorithm for joint spectrum allocation, interference mitigation, and energy efficiency optimization in Cognitive Radio–Vehicular Ad Hoc Networks (CR-VANETs). EH-HHO integrates adaptive exploration, dynamic switching, and crowding-distance preservation to avoid premature convergence and obtain well-distributed Pareto-optimal solutions. Extensive simulations, using the SUMO–OMNeT++, VEINS/ns-3, and hardware-in-the-loop experiments, show superior spectrum utilization, energy savings, and convergence speed compared to NSGA-II and MOPSO. Statistical validation confirms performance significance, highlighting the EH-HHO as an efficient non–deep learning framework for CR-VANET optimization.