# An Improved Pied Kingfisher Optimizer for Maritime UAV Path Planning
## 5 Conclusions  
UAV maritime rescue missions face challenges such as adverse sea conditions and the need for urgent response. In order to tackle these challenges, this paper proposes an improved Pied Kingfisher UAV path optimization algorithm, accompanied by a detailed analysis of its performance and practicality. The key findings are as follows:  
To address the issues of suboptimal solutions and slow convergence speed in the PKO algorithm, this paper proposes three improvement strategies. Firstly, an innovative refraction-based reverse learning mechanism is introduced during the update phase to augment population diversity and refine convergence accuracy. Secondly, a variable spiral sine-cosine search mechanism is employed within the global loop to harmonize exploration-exploitation weights and expedite convergence. Lastly, a Cauchy mutation strategy is incorporated into the escape phase to strengthen local escape ability. The improved algorithm was subjected to rigorous testing against seven traditional algorithms, including PSO and GA, on both CEC2005 and CEC2022 datasets. As shown in Figure 11, experimental results unequivocally show that the IPKO algorithm consistently secured first place across all test sets while significantly surpassing the comparison algorithms in terms of optimization accuracy and convergence speed.  
The IPKO algorithm was applied in this study to simulate the UAV maritime rescue scenario using the model constructed in Part 4. As shown in Table 8, the path cost of the IPKO algorithm is 119.97, indicating a reduction of 5.77% compared to the traditional PKO algorithm with a path cost of 127.32. Furthermore, when compared to the DOA algorithm with a path cost of 195.05, this reduction amounts to 38.49%. Consequently, it can be  
Figure 11: Ranking of various algorithms in different CEC tests.  
Figure 10: Three-dimensional path-planning trajectory diagram for UAVs.  
concluded that the IPKO algorithm effectively improves the efficiency of UAVs during maritime rescue missions.  
In conclusion, the proposed IPKO algorithm outperforms the compared algorithms in terms of both optimization accuracy and convergence speed, demonstrating its applicability and efficiency in complex maritime environments. This provides robust technical support for UAV maritime rescue missions.  
Conceptualization, W.C. and H.Y.; methodology, W.C.; validation, W.C., F.Y. and F.X.; formal analysis, W.C.; data curation, W.C.; writing--original draft preparation, W.C.; writing--review and editing, W.C., J.C. and X.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Equipment Development Department of the Central Military Commission, grant number 347C0B5D.This research was funded by Ningbo Municipal People's Government, grant number 2023J340.This research was funded by Ningbo Municipal People's Government, grant number 2023Z044.  
Not applicable.  
The experimental data have been presented within this paper. For any additional information or data inquiries, please reach out to the first author or the corresponding author.  
The authors declare no conflicts of interest.