Modern cities are facing increasing traffic congestion, necessitating the implementation of intelligent traffic management systems. One of the key areas in this field is adaptive traffic signal control, which can adjust to changing traffic conditions. However, existing methods for optimizing traffic signal cycle parameters have several limitations, such as high computational complexity, the risk of premature convergence of algorithms and the difficulty of accounting for traffic dynamics. This study proposes an approach to optimizing the characteristics of an intelligent transportation system using hybrid evolutionary algorithms. The methods we developed combine the principles of genetic algorithms (GA) and particle swarm optimization (PSO), enabling a balance between global and local search for optimal parameters. The research examines six different hybridization schemes, including modified versions of basic algorithms, as well as their integration with HDBSCAN clustering methods for adaptive optimization frequency tuning. To evaluate the effectiveness of the proposed algorithms, a simulation model was developed in the AnyLogic environment, replicating real urban traffic conditions. Numerical experiments conducted on a local section of the road network in Moscow demonstrated that the hybrid SlipToBest algorithm achieves the best results in reducing average travel time and fuel consumption, while the Alternating algorithm ensures high solution stability. The results of this study confirm the feasibility of using hybrid evolutionary methods for traffic flow management tasks. The proposed algorithms not only enhance the efficiency of traffic signal control but also establish a foundation for the further development of adaptive urban traffic management systems.