## 6 Conclusions  
This paper proposed a method for solving the real-time aiming strategy in SPT plants, utilizing an improved meta-heuristic optimization algorithm. Two new mechanisms were introduced into the classical PSO based on the mathematical model of a heliostat's physical IA. These mechanisms allowed the intelligence agent, directly associated with the physical heliostat, to receive status information from others in the region, adaptively update its state, and leverage the physical nature of heliostat operational continuity to facilitate the solution process. A simulation environment of a heliostat field is established as a case test to verify the performance of the algorithm proposed in this paper by comparing the optimization performance of six swarm intelligence optimization algorithms' dynamic mirror field real-time aiming strategy. The results demonstrated that our method achieved real-time optimization in heliostat field scenes with continuously changing cloud shadows.  
The population inheritance mechanism obtains 10 times the initial solution for Modified PSO and IUPSO, which greatly improves the solution efficiency of the algorithms, but the heliostat field scene becomes more complex, and the performance of the Modified PSO appears to be degraded. IUPSO physically models the heliostat individuals, as well as proposes a customized updating mechanism of the heliostat individuals to solve the problem and successfully realizes the complex. It successfully realizes the real-time optimization of the aiming strategy in complex heliostat field scenarios and reduces the computational cost. Tests on 400 real heliostat field scenarios show that IUPSO outperforms the improved PSO in all aspects and ensures robust real-time optimization even under complex conditions. A model for atmospheric attenuation was developed and implemented to assess its impact on the real-time scheduling optimization of heliostat field aiming strategies. Testing on a full-heliostat field simulation environment with a cloud movement path and heliostat scenes based on three different cloud locations confirmed the effectiveness of the IUPSO real-time aiming strategy. Future work should consider more realistic and complex cloud conditions. Combining machine learning with swarm intelligence optimization algorithms may significantly mitigate real-time scheduling errors in heliostat fields caused by cloud prediction inaccuracies. Additionally, the current atmospheric attenuation model only accounts for scattering and absorption. A more sophisticated model should be developed to enhance the realism and reliability of future studies.  
Y.W.: Methodology, writing--original draft, investigation, formal analysis, software, data curation, validation and visualization; Z.W.: writing--review and editing, supervision, funding acquisition, resources; D.N.: conceptualization, methodology, writing--review and editing, supervision, funding acquisition, resources. All authors have read and agreed to the published version of the manuscript.  
This research received financial support from the National Key Research and Development Program of China (No. 2021 YFF0500403), the program of China Scholarships Council (No.202306320449) to the National University of Singapore, and the program of A*STAR MTC YIRG 2022 Grant. (222K3024).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to data privacy principle.  
No author associated with this paper has disclosed any potential or pertinent conflicts that may be perceived to have impending conflict with this work.