(2017) employed the GA search technique to optimize the coefficients of the gamma function utilized in the EMS of an electric vehicle hybrid energy storage system in real-time. The results indicated that, compared to the rule-based strategy and the battery-only system, the strategy incorporating GAs and the strategy with fixed coefficients achieved a 40% reduction in the RMS current rate during the New European Driving Cycle without exceeding the nominal maximum current rate [37]. [PERSON] et al. (2022) optimized a fuzzy-controlled energy storage system for an electric vehicle hybrid energy storage system through the application of a GA management strategy. The results demonstrated that the energy economy was enhanced by 2.6%, 2.4%, and 3.3% at ambient temperatures of 10 \({}^{\circ}\)C, 25 \({}^{\circ}\)C, and 40 \({}^{\circ}\)C, respectively [38]. [PERSON] et al. (2023) utilized GAs to optimize the fuzzy logic control parameters, including the center of the subordinate function and the width of the fuel cell. The results indicated that the equivalent hydrogen consumption was reduced by 16.55% when compared to the GA-optimized fuzzy EMS, and by 40.50% when compared to the conventional fuzzy EMS. This significantly improved the total fuel economy of the fuel cell vehicle [39]. [PERSON] et al. (2024) optimized the fuzzy logic EMS parameters of the fuel cell HEV using GA. The simulation results validated the effectiveness of the proposed strategy, which involved a four-stage optimization process, including the adjustment of the affiliation function. This led to a substantial improvement in the fitness function, with fuel utilization increased by 28.5% and inefficient operation reduced by 13%, compared to the original EMS and thermostat strategy [40]. Furthermore, hybrid genetic algorithms (HGAs) demonstrate superior performance in addressing multi-objective, nonlinear optimization problems. This is primarily attributed to their enhanced time efficiency, which stems from the utilization of particle swarms. These swarms mitigate the computationally intensive task of generating and refining candidate solution populations, thereby diminishing the likelihood of premature convergence. Additionally, HGAs incorporating a population intelligence mechanism or local search strategy effectively mitigate the limitations associated with local optima and premature convergence. This hybrid optimization strategy not only expedites convergence but also yields a more robust solution. For instance, [PERSON] et al. (2018) introduced a hybrid genetic algorithm leveraging NKCV2 and PX to achieve efficient clustering with O(N) complexity. Experimental results indicate an 18.6% improvement in clustering purity compared to traditional genetic algorithms, a 41% enhancement in efficiency relative to DBSCAN, and a one-third reduction in parameter sensitivity compared to DP. This algorithm innovatively addresses challenges such as clustering arbitrary shapes, automatically determining cluster counts, and ensuring noise robustness. Theoretical validation of the local search strategy reveals a 37% acceleration in convergence, offering a novel approach for clustering complex data [41]. [PERSON] et al. (2025) proposed a multi-intelligence hybrid genetic algorithm (MA-HCAGA) that innovatively combines binary-coded genetic operators, bi-objective discrete particle swarm optimization, and fuzzy clustering techniques. The algorithm effectively addresses the bi-objective optimization challenge of maximizing vehicle flow and minimizing network complexity in reconfigurable multi-layer road networks. This is achieved by orchestrating intelligence interactions through finite state machines. Experimental findings demonstrate that the method selects the optimal topology along the Pareto front and substantially enhances road network capacity [42].