## 1 Introduction
An alternative approach leverages reinforcement learning to cultivate UAV swarms with autonomous decision\(-\)making capabilities, enabling real\(-\)time task objective assignment and flight path planning. [PERSON] (2023), based on image recognition and reinforcement learning algorithms, studies the path assignment of UAVs using visual navigation, and beats human champions in an FPV test [15]. [PERSON] (2022) studied the real\(-\)time planning problem of UAV tracking dynamic targets in a large\(-\)scale unknown environment using the deep reinforcement learning method [16]. Regarding the verification and matching of drone platform performance and electro-optical payload indicator performance, they are typically decoupled from platform design and task requirements. From task planning to drone design, payloads are often considered optional accessories, making it challenging to achieve optimal metrics based on swarm tasks. Currently, only some studies comprehensively verify the relationship between electro-optical payloads, drone performance, and task requirements. Generally, these investigations are based on optimization concepts, modeling the payload capability\(-\)task requirements, and seeking optimal metrics under objectives such as timeliness and energy consumption [17; 18]. [PERSON] (2015) calculated the camera's coverage width, factoring in the field width of the optical sensor, the camera lens' focal length, and the distance between the camera and the ground, subsequently crafting an area coverage path that minimizes task time [19]. [PERSON] (2016) designed an overlay path\(-\)planning algorithm with the objective of low energy consumption, grounded on an actual energy model and image resolution constraints, additionally proposing two energy\(-\)conscious safety mechanisms for returning home [20]. [PERSON] (2022) formulated a multi\(-\)objective mathematical model aiming to minimize both flight path length and correction frequency, considering the path\(-\)tracking deviation and turning radius of UAVs during offshore platform patrols while introducing a UAV path planning strategy rooted in genetic algorithm principles [21]. Targeting UAV search and rescue operations, [PERSON] (2023) investigated area coverage search techniques and task parameter configurations under the influence of sector scanning photoelectric load, comparing the efficacy of different optimization algorithms in the search and rescue frameworks involving sector photoelectric devices [22]. These aforementioned studies have actively examined the intricacies of UAV swarm task planning, yielding fruitful results. However, their primary emphasis has been on scenarios featuring a predetermined count of UAVs, neglecting critical variables such as drones' quantity and payload capacities. In real\(-\)world applications, the imperative lies in ascertaining the optimal drone count for deployment and effectively allocating target routes for each drone, especially in situations involving time\(-\)intensive and multifaceted missions. The key contributions of this paper can be summarized as follows:  
* In contrast to traditional task\(-\)planning frameworks, this study establishes a unified framework for UAV swarm search task\(-\)planning and payload performance optimization. On the two\(-\)dimensional level, it mainly conducts optimal task scheduling and outputs a two\(-\)dimensional projection of the three\(-\)dimensional trajectory. Building upon this, considering drone performance and electro-optical payload performance, the problem is elevated to three dimensions. A thorough examination is conducted on how these performance indicators impact task completion capability, providing results from optimal index analysis. * An improved cluster algorithm is devised based on target geographic distribution. The inner layer of the algorithm introduces an ant colony optimization algorithm, only considering the optimal trajectory of the entry and search segments.