## 1 Introduction  
Unmanned Aerial Vehicle (UAV) swarm systems, encompassing an aggregation of drones collaborating in tandem to execute multifaceted tasks, represent an innovation with considerable promise in diverse sectors, including urban logistics, maritime surveillance, border security, and regional search and rescue operations. Especially pertinent to time--critical regional searches with numerous targets, deploying a cost--effective UAV swarm in the specific zone could augment the numerical advantage, thereby expediting the task completion time. In managing UAV swarms, the control hierarchy is typically divided into four stratified layers: the task assignment layer, the trajectory planning layer, the trajectory tracking layer, and the base control layer. The foremost layer, task assignment, involves designated personnel selecting tasks and issuing task commands. The second layer, the flight path planning layer, employs a designated trajectory planning algorithm to formulate optimal flight paths for either the collective swarm or individual units. The third layer, the trajectory tracking layer, takes the coordinated trajectory as the desired path and uses trajectory tracking control methods to generate attitude guidance commands. The fourth layer comprises the UAV's individual control system, which formulates respective control directives for the rudder surface. Nevertheless, when deploying drone swarms in practical applications faced with specific tasks and an unknown numbers of drones, determining how to optimally allocate drones based on mission timelines, coverage requirements, and matching optimal platforms and payload capacities to enhance the efficiency of the drone swarm remains a research--worthy challenge. The existing literature predominantly assumes the number and payload capacity of UAVs as known variables. These studies often involve only a few drones, not addressing the task scheduling for large\(-\)scale drone swarms, and seldom delve into the analysis of task payload suitability. Addressing this gap necessitates the consideration of two pivotal factors. From the perspective of matching swarm size, the number and routes of the drones need to be as optimal as possible to meet the task requirements. From the viewpoint of matching payload indicators, taking an electro-optical payload as an example, it is essential to jointly verify the impact of drone performance and electro-optical payload capabilities on task requirements [1; 2; 3; 4; 5; 6]. Through scrutinizing UAV swarm task planning, we aim not only to formulate algorithms for superior task planning, but also to evaluate the interaction between UAV platforms and payload performances on overall mission effectiveness. This analytical approach facilitates the validation of the feasibility and efficacy of designated indices. This nuanced approach thereby enhances UAV swarm task\(-\)planning optimization, taking into account UAV numbers and routes, as well as the aptitude of mission loads. Two prevalent strategies are generally adopted to solve the problem of UAV swarm task assignment and track planning. The first employs optimization algorithms to transmute the issue into either an offline or online optimization challenge, premised on constraints such as time, energy, or obstacle avoidance, given a known UAV quantity and unknown targets [7; 8; 9]. For example, [PERSON] (2017) studied the UAV track planning problem under the minimum search time target based on the ant colony optimization method [10]. [PERSON] (2021) studied the problem of optimal communication relay for UAV clusters based on ant colony and clustering methods [11]. Commonly used optimization algorithms include the particle swarm optimization algorithm, crow search optimization algorithm, and more advanced gray wolf optimization algorithm, artificial hummingbird optimization algorithm and Harris Eagle optimization algorithm, etc. These algorithms have solved many practical problems in the engineering field and achieved good results [12; 13; 14].