# Depth Optimization Analysis of Articulated Steering Hinge Position Based on Genetic Algorithm
## 1 Introduction  
A large number of steering experiments of wheel loaders show that there is always obvious pressure fluctuation in steering systems [1; 2; 3]. Due to the frequent steering operation of wheel loaders, the long-term existence of this phenomenon impacts hydraulic components, produces vibration and noise, and seriously affects the stability and reliability of steering systems [4; 5; 6]. [PERSON] [7] established the mathematical model of steering systems by using equivalent parameters. Starting from the frequency characteristics of the system and the stability of the system, the pressure oscillation phenomenon of the steering hydraulic system was analyzed, and the causes and influencing factors of the pressure fluctuation during steering were determined. [PERSON] [8], in a study based on the LG953 wheel loader, used the SQP algorithm to optimize the hinge position of steering mechanisms, and obtained the corresponding stroke difference and arm of force difference curves. However, in the SQP algorithm, it is easy to enter the local optimal solution, and it has fewer added constraints, which affects the selection of the optimal solution. The objective function of [PERSON] [9] was to minimize the stroke difference and the power of the oil pump. The steering mechanism was optimized by using the mixed penalty function method, so that there was no stroke difference in the optimized steering mechanism. However, the steering resistance distance was derived from empirical formulas, and theinfluence of steering angle on the steering resistance distance was not considered [10; 11]. The above analysis did not consider the influence of the stroke difference and arm of force difference caused by the position of the hinge point of the steering mechanism on the pressure fluctuation of the steering system. In this paper, the reasonable position of the hinge point of the steering mechanism is also found, the relationship between the stroke difference and the arm of force difference is analyzed, and the main influencing factors of the pressure fluctuation of the steering system are found. On the premise of satisfying the space limitation and driving moment, the coordinates of many groups of points can be obtained. There must be a set of coordinate points in these groups of point coordinates that minimizes the stroke difference and arm of force difference of the steering mechanism and finds the optimal solution among many solutions. The genetic algorithm (GA) is a useful algorithm for reserving useless or removing simulated biological evolution in the process of optimization [12]. It can be used to search for the optimal articulation coordinates [13; 14; 15]. The GA starts from the collection of clusters and has a large coverage, which is conducive to global optimization. It can deal with multiple individuals in a group at the same time and reduce the risk of falling into the local optimal solution. The GA can optimize the solution of the problem generation after generation, and approximate the optimal solution. [PERSON] [16] aimed to solve the vehicle routing problem with time-limited windows by using GA to minimize the total distance and the total number of vehicles. The optimization results of the GA were verified by experiments. [PERSON] [17] finished the preform optimization for hot processes using GA, and his study demonstrated that a GA-based approach could realize the optimization effectively. [PERSON] [18], aiming at the job shop scheduling problem and taking the maximum lead time and the minimum tardiness as the optimization objectives, obtained the solution method of the GA and verified the effectiveness of the algorithm by simulation. Optimal results can be obtained by only using the stroke difference function and arm of force difference function as objective functions; however, the steering system pressure will increase and energy will be wasted. Therefore, the selection of the objective function was the key to getting the optimal solution of the GA in this paper.