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With the increasing number of electric vehicles (EVs), intelligent management of EV charging is essential to maintain the balance of the electrical grid. This article presents an advanced method for optimizing EV charging using meta-heuristic tools such as genetic algorithms (GA), Ant Colony Optimization (ACO), and a GA-ACO hybrid approach. The results indicate that integrating these techniques effectively reduces the power demand on the grid while meeting the charging requirements of EVs. The GA-ACO hybrid method, in particular, demonstrates superior performance by smoothing power demand and minimizing significant fluctuations compared to individual approaches. This study highlights the importance of combining optimization techniques to enhance the stability and reliability of EV charging management.
