## 4 Conclusions  
This study utilizes DSPA to reconfigure the ship power system network, initially presenting a population exchange strategy based on the golden ratio and detailing the algorithm's operational process. The population is divided into primary and auxiliary search groups, wherein particles with higher fitness values in the auxiliary group are exchanged with lower-fitness particles in the primary group according to the golden ratio. This strategy preserves population diversity and effectively mitigates the risk of the algorithm converging to a local optimum. With the addition of chaotic initialization and discretization, the ring power system model introduced in Section 2 serves as the simulation object. DSPA is then employed in simulations of the ship power system and compared with CGA, PSA, and IPSA. Experimental results demonstrate that the DSPA-enhanced approach offers significant advantages, particularly in minimizing switching operations and achieving earlier convergence generations. This method not only provides  
Figure 14: Improved particle swarm simulation results: (**a**) improved particle swarm algorithm adaptation value change; (**b**) improved particle swarm algorithm optimal switching coding.  
Figure 15: Simulation results of DSPA: (**a**) DSPA fitness changes; (**b**) optimal solution load switch code of DSPA.  
an effective reconfiguration scheme but also ensures convergence with a probability of one. The findings confirm that DSPA effectively addresses the reconfiguration challenge in ship power systems, offering notable advantages and achieving the anticipated results.  
In the process of research in this paper, there are still many problems that need to be studied in depth; after summing up, the future research direction of this topic is as follows:  
1. The power system network reconfiguration of ship power system failure occurs after the location of faults in the ship power system; the ship power system can be damaged before the predictive reconfiguration is able to greatly improve the ship's vitality, so the predictive network reconfiguration of the ship's power system is an important research direction.
2. With the continuous development of ship integrated power systems, the complexity of the ship power grid is also gradually increasing. How to carry out an accurate mathematical description of the complex power grid and the establishment of appropriate constraints are further important research directions.
3. Chemical attack, electromagnetic attack, and other attack methods should also be taken into consideration.  
Conceptualization, K.M. and J.Z.; methodology, K.M. and Z.X.; software, K.M. and Z.X.; validation, K.M., Z.X. and A.Z.; formal analysis, K.M. and Z.X.; investigation, K.M. and S.W.; resources, J.Z.; data curation, K.M., Q.Z. and J.P.; writing--original draft preparation, K.M.; writing--review and editing, K.M. and Z.X.; visualization, K.M., Z.X. and A.Z.; supervision, J.Z.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the project Research and application of Smart Ship Digital Twin Information Platform (funding number: 2022 JH1/10800097).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
All authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.