###### Abstract  
Optimizing the heliostat field aiming strategy is crucial for maximizing thermal power production in solar power tower (SPT) plants while adhering to operational constraints. Although existing approaches can yield highly optimal solutions, their considerable computational cost makes them unsuitable for real-time optimization in large-scale scenes. This study introduces an efficient, intelligent, real-time optimization method based on a meta-heuristic algorithm to effectively and reliably manage SPT plant operations under varying solar conditions, such as cloud shadowing variations. To minimize redundant calculations, the real-time optimization problem is framed in a way that captures the operational continuity of the heliostat, which can be utilized to streamline the solution process. The proposed method is tested in a simulation environment that includes a heliostat field, cylindrical receiver, and cloud movement model. The results demonstrate that the algorithm presented in this paper offers higher intercept efficiency, improved robustness, and reduced optimization time in more complex scenes. heliostat aiming strategy; meta-heuristic algorithm; real-time optimization; solar power tower +
Footnote †: journal: Applied  
0000-0001-9999-4000-0000-0000-0000-0000-0000-00000-0000-00000-0000-000  
to develop effective and efficient aiming strategies by modifying the early optimization methods for static aiming strategy problems to improve the performance of SPT plants and achieve a more uniform flux distribution. For example, [PERSON] et al. introduced an aiming factor \(k\), multiplied by the heliostats beam radii, to extend the static Vant-Hull aiming [21, 22]. In addition, [PERSON] and [PERSON] [23] introduced an additional factor \(k_{3}\) to divide the field into radial sectors for those located further away from the tower to smooth the flux profile. However, heliostat fields are subject to varying meteorological conditions around the world [24], especially cloud shadows. Optimization methods for static problems cannot accommodate these dynamic changes, leading to the development of dynamic aiming strategy optimization methods to optimize the aiming strategy of the heliostat fields based on meteorological conditions. The research into dynamic aiming strategy optimization for central receiver systems has been ongoing for some time, and various approaches have been proposed. Some of the earliest work, by [PERSON] et al. [25, 26], involves fast heuristic aiming strategies that maximize the heat flux hitting the receiver while distributing the aim points of the heliostats in specific patterns. These strategies are part of the DELSOL optics simulation software, an optimization tool for central receiver system design. [PERSON] et al. [27] used a heuristic approach to approximate a predefined heat flux distribution, and [PERSON] et al. [28] partitioned the heliostat field into small groups to reduce peak heat flux densities. [PERSON] et al. [29] developed an accelerated aiming strategy that can be used for dynamic scenarios, such as short-term environmental influences. The primary challenge of solving the aiming strategy for dynamic heliostat fields is the increased computational complexity of real-time computation and heightened calculation amount due to dynamic cloud shadowing. Dividing the full heliostat field into sector fields and using machine learning algorithms that rely on pre-training and meta-heuristic optimization can provide improved real-time solutions. The field of aiming strategy optimization is advancing with the development of machine learning algorithms [30], including supervised and reinforcement learning paradigms. The former paradigm learns mapping by minimizing the difference between the model output and the optimal label, using various methods such as Hopfield networks [18], Elastic Nets [31], Pointer Nets (PN), and fully convolutional networks to solve optimization problems [32].