# The Intelligent Layout of the Ship Piping System Based on the Optimization Algorithm
## 4 Simulation Experiments and Analysis
### Single Pipeline Simulation  
#### 4.2.1 Single Pipeline Parameter Setting  
The selection of control parameters in the algorithm is very critical, and the different selection of parameters will have a very significant impact on the performance of the algorithm. The ideal range of algorithm parameters should be able to ensure that the algorithm can converge to a better solution in a reasonable time while maintaining the diversity of the population to avoid premature convergence and falling into local optimal solutions. Determining the ideal range of algorithm parameters usually requires experimentation and tuning. Based on previous studies and hundreds of simulation experiments simulation experiments, the optimal range of the algorithm parameters in this paper is determined. Among them, the ideal size of the population size \(d\) is 20-150. The value of the maximum iteration number \(g_{\text{max}}\) should be taken to ensure that it is not less than the average value of the iteration number at convergence. The ideal size of the crossover probability \(P_{\text{c}}\) is 0.6-1.0. The ideal size of the variation probability \(P_{\text{m}}\) is 0.005-0.15. The ideal value of the selection probability \(P_{\text{s}}\) is 1.0 to ensure that the population size does not change during the iterative evolutionary process. The ideal range of the similarity threshold \(\varepsilon\) is 2-4. The ideal range of the concentration value influence coefficient \(\lambda\) is 0.001-0.2. To verify the optimized A*-genetic algorithm proposed in this paper in the actual pipeline intelligent arrangement, the optimized A*-genetic algorithm, the A* algorithm of [PERSON] [18], the particle swarm algorithm of [PERSON] [21], and the maze-genetic algorithm of [PERSON] [26] are used to search for the optimal solution of the pipeline in the set pipeline arrangement space. The specific basic parameter settings of each algorithm are shown in Table 3.  
Figure 8: Pipe layout space diagram. (**a**) Side view of piping arrangement space; (**b**) Top view of piping arrangement space. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **PSO** & **MA-GA** & **A*-Bian** & **A*-GA** \\ \hline \(d\) & 50 & 50 & / & 50 \\ \(g_{\text{max}}\) & 500 & 500 & / & 500 \\ \(P_{\text{c}}\) & 0.85 & 0.85 & / & 0.85 \\ \(P_{\text{m}}\) & 0.05 & 0.05 & / & 0.05 \\ \(P_{\text{s}}\) & 1.0 & 1.0 & / & 1.0 \\ \(\alpha\) & 0.2 & 0.2 & 0.2 & 0.2 \\ \(\beta\) & 0.4 & 0.4 & 0.4 & 0.4 \\ \(\gamma\) & 0.4 & 0.4 & 0.4 & 0.4 \\ \(T\) & 400 & 400 & 400 & 400 \\ \(\varepsilon\) & / & / & / & 2.5 \\ \(\lambda\) & / & / & / & 0.012 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Solving several algorithm parameter values of a single pipeline. The optimized A*-genetic algorithm proposed in this paper, the A* algorithm of [PERSON] [18], the particle swarm algorithm of [PERSON] [21], and the maze-genetic algorithm of [PERSON] [26] are each repeatedly run 15 times in the set pipeline arrangement space for the intelligent arrangement of pipelines. The specific results are shown in Table 5. The following conclusions can be obtained from the comparative analysis of the pipeline arrangement information obtained in Table 5:  
1. The quality of the intelligent arrangement of the ship pipeline: firstly, the average length of the pipeline path obtained by the A*-genetic algorithm is the shortest, the average number of elbows is the smallest, the average energy value is the lowest, and the average adaptability value of path is the highest, i.e., it best meets the actual requirements of ship pipeline arrangement. Secondly, the number of times that A*-genetic algorithm searches for the optimal solution ranks first among the four algorithms, i.e., it has the highest probability of searching for the optimal solution of pipeline arrangement. The probability of obtaining the optimal solution of the piping arrangement is the highest.