# Mobile Robot Path Planning Based on Fused Multi-Strategy White Shark Optimisation Algorithm
## 4 Experimental Simulation  
### Algorithm Testing and Analysis  
To evaluate the performance of the improved white shark algorithm, this paper selected five representative algorithms for comparison with the FMWSO algorithm in simulation experiments. These include the Particle Swarm Optimization (PSO) algorithm, which demonstrates strong global search capabilities; the Simulated Annealing (SA) algorithm, which excels in local search capabilities; the traditional White Shark Optimization (WSO) algorithm; the classic improved White Shark Optimization algorithm, the Hybrid White Shark-Whale Optimization (HWSO) algorithm; and the latest improved White Shark Optimization algorithm based on deep learning, the Improved White Shark Optimization (IWSO) algorithm. In order to guarantee the fairness of algorithm comparison, the population size of all algorithms N = 50, the dimension of the function dim = 30, and the number of iterations t\({}_{\max}\) = 500. The information for each algorithm is shown in Table 1, which shows the results of 30 independent runs. This paper selects four functions from internationally recognized test functions as test functions, with the standard test functions shown in Table 2. The four selected test functions are highly representative: the Sphere function is a single-peaked convex function, simple and easy to optimize; the Schwefel function is a multi-modal non-convex function with numerous local optima and a unique global optimum position, testing global exploration capabilities; the Rastrigin function is a highly multi-modal function with local optima growing exponentially and severe oscillations, making it extremely challenging: The Ackley function is a multi-modal function with a relatively large number of local optima distributed uniformly, requiring a balance between global exploration and local fine-grained search. These four functions, ranging from simple to complex, are classic test functions for evaluating the performance of optimization algorithms. The trend of the fitness curve is used as an evaluation metric for the convergence speed of the algorithm; the optimal value, worst value, mean, and standard deviation are used as evaluation metrics for the algorithm's search capability, search stability, and dispersion, respectively. Figure 5 shows the fitness change curves of six algorithms on four types of test functions: Sphere, Schwefel, Rastrigin, and Ackley. The FMWSO algorithm demonstrates a significant advantage: When faced with the single-peaked convex function Sphere, the FMWSO algorithm initially leverages its chaotic elite pool strategy to achieve exploration efficiency that surpasses PSO after 50 iterations, which tends to stall, while SA converges slowly and inefficiently throughout the process. HWSO and IWSO, though faster in the later stages, are slower in the initial phase, with the FMWSO algorithm being the fastest to lock onto the optimal direction. In the multi-peak high-dimensional Schwefel function, it continuously explores globally for better solutions using the \"cruising + sprinting\" mechanism, outperforming PSO, which easily becomes prematurely stagnant; SA, which has weak resistance to premature stagnation and gets stuck in local optima; WSO, which lacks exploration depth and stagnates early; HWSO, whose search power declines in the \begin{table}
\begin{tabular}{l l} \hline \hline
**Algorithm** & **Main Parameter** \\ \hline PSO & \(w=0.8,c_{1}=c_{2}=2\) \\ SA & \(T_{0}=100,\alpha=0.98\) \\ WSO & \(r=0.3,d=0.1,s=s_{\max}-(s_{\max}-s_{\min})\times\,t/T\) \\ HWSO & \(a=2-2\times\,t/T,w=0.5,r=0.3,d=0.1,b=1\) \\ IWSO & \(\beta=0.5,s=0.05,\eta=0.005\) \\ FMWSO & \(r=0.3,d=0.1,s=s_{\max}-(s_{\max}-s_{\min})\times\,t/T,T_{0}=100,\alpha=0.98\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main parameter. later stages; and IWSO, whose global cruising strategy has low flexibility.