## 1 Introduction
The application of optimization algorithms in the field of bridge engineering can solve design problems with room for improvement, thus significantly increasing the accuracy of the analysis, helping engineers to better understand and predict the behavior of bridges, and improving the efficiency and safety of bridge design and maintenance [9; 10; 11]. Commonly used optimization algorithms include the genetic algorithm, the particle swarm optimization algorithm, the simulated annealing algorithm, and so on [12; 13]. The genetic algorithm is a computational model used to search for the optimal solution by simulating the natural evolution process and has good robustness and global search ability when solving multi-objective optimization problems [14; 15]. In terms of the load and responses of bridges, the application of the genetic algorithm mainly includes load model calibration, damage identification, and reliability prediction. By calibrating the structural model through the genetic algorithm based on measured data, the static and dynamic characteristics of the model can be closer to the characteristics of the actual structure [16]. By using the genetic algorithm together with other methods to capture the change in structural properties or responses to load, the damage of bridge structures can be identified, and the optimal design parameters can be obtained [17; 18; 19]. The reliability analysis based on the genetic algorithm can skillfully identify the critical failure modes of the bridge components or the whole structure and estimate the probability of failure in an effective and accurate way, which offers the decision maker an important reference for making practical solutions [20; 21; 22; 23]. In addition, the improved genetic algorithm can also be used to optimize the arrangement of the measurement points of bridge monitoring sensors, layout of tendons for prestressed concrete, or realize the mathematical optimization course of cable force, so as to improve the working efficiency and reduce the cost [24; 25; 26]. The purpose of this paper is to study the traffic load effect and traffic load model optimization of the suspenders of the super-long-span suspension bridge by theoretical analysis and numerical simulation. In Section 2, taking a suspension bridge with a main span of 2300 m as an example, the traffic load effects of the main components are analyzed. In Section 3, a classification method of a suspender based on the geometric feature analysis of the influence line is proposed, and the extreme load effect scenario and load level of the bridge under the action of heavy vehicles are analyzed. On this basis, three optimization objectives of accuracy, convenience, and improvement are proposed in Section 4. Multi-objective optimization based on the genetic algorithm is adopted to optimize the traffic load models of the different types of suspenders of the bridge, and finally the optimized traffic load models for the different types of suspenders are obtained. In Section 5, the optimization results are analyzed and discussed, and conclusions are drawn in Section 6. The research results and conclusions can provide a useful reference for the precise design, operation management, and maintenance of similar long-span suspension bridges.