## 4 AI-Based Solving Methods for V2G Optimization  
V2G optimization problems typically involve complex optimization tasks related to managing the bidirectional flow of energy between EVs and the grid. These problems often involve factors such as vehicle availability, grid demand, charging station capacities, energy prices, and user preferences. Many V2G optimization problems are indeed NP-hard due to their combinatorial nature and the need to find optimal solutions among a large number of possible configurations [27]. Some research, such as that conducted by [PERSON] et al. [28], has mentioned the feasibility and popularity of GA and PSO as highly utilized approaches for addressing V2G problems. However, these two methods are not without limitations. One notable shortcoming is the low convergence efficiency, where the algorithms may converge to sub-optimal solutions before thoroughly exploring the solution space. Additionally, both GA and PSO may struggle with scalability when dealing with large-scale V2G systems or complex optimization objectives [29]. Intelligent x-heuristic algorithms offer several advantages when addressing optimization challenges in V2G systems. Firstly, simulation-based approaches provide greater flexibility in modeling complex systems and incorporating various constraints and objectives under uncertainty scenarios [30]. Simulation-based algorithms can integrate detailed simulation models of V2G systems, capturing the intricacies of vehicle behavior, grid interactions, and uncertainties in external factors such as weather conditions and user preferences. This modeling approach enables a more accurate representation of real-world problems and facilitates the exploration of diverse solution spaces. Secondly, simulation-based algorithms are well suited for optimizing V2G systems in dynamic and uncertain environments. By utilizing stochastic simulation methods, these algorithms can effectively capture uncertainties and variations in system behavior, allowing for robust decision-making under uncertainty [31]. Applications of AI-based techniques, such as simheuristic, learnheuristic, and agile optimization, bring an unprecedented level of originality to V2G optimization. For instance, simheuristic can combine simulation and heuristics to model uncertainties and optimize energy exchanges, while learnheuristics use machine learning to predict patterns and \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Problem Characteristics** & **Solution Approach** \\ \hline [PERSON] and [PERSON] [16] & SO & EM \\ [PERSON] et al. [17] & MO & EM, AM \\ [PERSON] et al. [18] & MO & PSO \\ [PERSON] et al. [19] & SO & GA \\ [PERSON] et al. [20] & MO & BPSO+ACO \\ [PERSON] et al. [21] & MO, CSP & QLSA \\ [PERSON] et al. [22] & MO & ALO \\ [PERSON] et al. [23] & MO, UN & FFA \\ [PERSON] et al. [24] & MO, UN & MCO, AVM, GA \\ [PERSON] et al. [11] & MO, UN, FL & NN+PSO \\ [PERSON] and [PERSON] [25] & MO, DC & QLA \\ [PERSON] et al. [24] & MO, DC, FL & EHO \\ \hline \hline \end{tabular} Note: SO = Single Objective, MO = Multi-Objective, CSP = Charging Stations Placement, UN = Uncertainty, FL = Fuzzy Logic, DC = Dynamic Conditions, EM = Exact Method, AM = Approximate Method, PSO = Particle Swarm Optimization, GA = Genetic Algorithm, BPSO = Binary PSO, ACO = Ant Colony Optimization, ALO = Antilon Optimization, QLSA = Quantum Lightning Search Algorithm, FFA = Firefly Algorithm, MCO = Monte Carlo Optimization, AVM = Anthetic Variates Method, NN = Neural Network, QLA = Q-Learning Algorithm, EHO = Elefant Herding Optimization.  
\end{table} 
Table 1: Summary and classification of revised V2G systems references. improve decision-making. Agile optimization [32] ensures rapid adaptation to real-time data, making V2G systems more responsive and reliable. These techniques optimize energy distribution, support renewable energy integration, and contribute to a more sustainable and resilient power grid.