# Hybrid Improved PSO Algorithm for Soil Property Parameter Estimation
## 4 Simulation Results and Discussion
### Discussion  
First, the simulation results in Table 7 show that the error rate of the improved algorithm is kept within 10%. The average computation time of the PSO-EDO algorithm is 14.5% shorter than that of the EDO algorithm and 18.0% shorter than that of the PSO-WOA algorithm. Second, as observed in Figure 5, although the improved algorithm has a relatively high fitness in the early iterations, its convergence speed is close to that of the PSO-WOA algorithm, with convergence nearly completed within 10 iterations. In terms of fitness at convergence, the PSO-EDO algorithm achieves a lower fitness value compared to the other two algorithms.  
From the above, the following conclusions can be drawn: In the parameter estimation simulation, the improved algorithm reduces the computation time compared to the other two algorithms without sacrificing computational accuracy or convergence speed. After completing the iterations, it achieves a lower fitness value, demonstrating that the PSO-EDO algorithm offers higher precision and convergence speed. The performance improvement of the algorithm lies in combining the strengths of the EDO and PSO algorithms. The EDO algorithm introduces higher randomness during the exploration phase, reducing the risk of becoming trapped in local optima. Meanwhile, the PSO algorithm offers faster convergence, and dynamically adjusting the inertia weight using Equation (20) enhances convergence speed, reduces computation time, and improves accuracy.  
In the study of soil property parameter estimation, optimizing complex mathematical models is often required. Improving computation speed while maintaining accuracy shortens the research cycle and facilitates real-time acquisition of soil property data. In the fields of artificial intelligence and logistics, faster computation translates into savings in computing resources, energy, and human resources. Therefore, this research holds significant practical value.  
In summary, applying the PSO-EDO algorithm to soil property parameter estimation successfully achieves efficient and accurate parameter estimation, and the simulation results validate the feasibility of this method.  
However, some limitations and application challenges should be acknowledged. First, although the algorithm performs well under the tested conditions, its estimation accuracy may degrade in highly heterogeneous or moisture-sensitive soils, where the underlying physical parameters exhibit strong nonlinearity or spatial variability. Second, the scalability of the PSO-EDO algorithm in large-scale, high-resolution terrain datasets remains to be validated, as increased data dimensionality and volume may impose additional computational burdens and memory requirements.  
Figure 5: Fitness line graph for the first 30 iterations of the three algorithms.  
Moreover, the current validation is based on simulation data under idealized conditions, which may not fully capture the complexity of real-world environments. In field applications, factors such as sensor noise, incomplete data, and dynamic terrain changes can influence algorithm performance. Additionally, actual terrain conditions often involve irregular profiles and variable soil-vehicle interactions that are difficult to model precisely. Therefore, future research should focus on validating the PSO-EDO algorithm using field data, exploring its integration with onboard systems for real-time soil parameter estimation, and evaluating its robustness under varying environmental conditions.