# An Improved Cuckoo Search Algorithm and Its Application in Robot Path Planning
## 6 Conclusions  
The primary contributions of this manuscript are as follows: Firstly, BASCS initializes the population using tent chaotic mapping and random migration, ensuring a uniform distribution and extensive search space coverage, thereby preventing premature convergence and local optima entrapment. Ablation studies on 10 benchmarks validate this approach's effectiveness. Secondly, BASCS integrates the direction judgment of BAS with Levy flight to accelerate search speed and enhance optimization precision. Thirdly, in later iterations, SCA is utilized instead of Levy flight to further improve accuracy. Furthermore, BASCS also makes adaptive adjustments to the Levy flight's step size and the cuckoo eggs' discovery probability, allowing for continuous evasion of local optima and improved optimization performance. Comparisons with CS and its variants on 10 benchmarks highlight BASCS's advantages. Additionally, the manuscript establishes BASCS's efficacy in robot path planning through comparative experiments, confirming its practical applicability in this area.  
The proposed approach has several significant limitations that deserve in-depth exploration. First, its computational complexity exceeds that of a traditional approach, which may hinder scalability to larger datasets. Second, the benchmarks utilized in our study may not comprehensively represent the diversity of real-world scenarios, potentially limiting the generalizability of our findings. Lastly, while the algorithm demonstrates considerable potential in controlled experiments, its performance in complex real-world applications remains to be validated.  
Looking ahead, future research should focus on several key areas to enhance the effectiveness and applicability of BASCS. First, developing strategies to reduce the algorithm's computational complexity is crucial for enabling efficient handling of larger datasets and facilitating its use in real-time environments. Second, expanding the benchmark suite to include a broader range of diverse and complex scenarios will provide a more comprehensive evaluation of the algorithm's generalizability and robustness. Additionally, conducting case studies to assess BASCS's performance in real-world applications will yield valuable insights into its practical utility, particularly in fields beyond robot path planning. While these enhancements may result in a slight increase in computational complexity, the algorithm's robust optimization capabilities make it a promising candidate for various applications. Therefore, future investigations should prioritize methods that balance these improvements with the need for computational efficiency, ensuring that BASCS remains a viable option across diverse operational contexts.  
W.M.: conceptualization and writing--original draft preparation; L.M.: methodology and writing--review and editing; B.Y.: validation and supervision; S.L.: formal analysis and resources. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number No. 62266019, and the Natural Science Foundation of Hunan Province, China, grant number No. 2024 JJ7394.  
Data Availability Statement: Data is contained within the article.  
I would like to thank [PERSON] for his contributions to the project administration and data curation sections of the manuscript.  
The authors declare no conflicts of interest.