## 1 Introduction  
UAV swarm intelligence technology is a technology based on biological swarm behavior that realizes autonomous decision-making and intelligent division of labor among UAVs through perception interaction, information transfer, and cooperative work among UAVs and jointly accomplishes a specific task cost-effectively in a complex environment [1; 2; 3]. Depending on efficient data collection, analysis, and collaborative processing capabilities, UAV swarm technology has diverse applications in agriculture and forestry, including but not limited to crop monitoring, disease prevention, forest fire response, and precision plant protection [4; 5; 6; 7]. With the advantages of cooperative operation, flexible adaptation, wide coverage, and cost-effectiveness, UAV swarm technology has a broad application prospect in the forest fire detection scenario. [PERSON] (2023) proposed a hybrid algorithm based on the fireworks algorithm and a genetic algorithm for UAV forest fire reconnaissance task assignment [8]. [PERSON] (2023) studied forest fire identification in UAV imagery using x-mobilenet [9]. [PERSON] (2023) researched a forest fire monitoring method based on UAV visual and infrared image fusion [10]. [PERSON] (2023) proposed a hybrid algorithm for UAV path planning for rescue in bushfire environments [11]. However, previous approaches had less research on a systemic forest fire detection strategy based on UAV swarms, including fire point location, fire situation identification, and fire control. In forest fire detection applications, UAV swarms carrying cameras and other sensors, utilizing self-organizing network communication technology, biome-like formation technology, and image recognition technology, should quickly reach forest fire starting points, accurately identify fire development, and make control measures. In complex forest environments, it is particularly important to rationally plan the course of action of UAV swarms so that they can quickly and accurately reach the fire point. The particle swarm optimization (PSO) algorithm and the artificial bee colony (ABC) algorithm are two commonly used UAV path planning algorithms [12; 13; 14; 15; 16]. The PSO algorithm is a population intelligence optimization algorithm that simulates the feeding behavior of a flock of birds, features a fast speed of convergence, and is easily trapped in local optimal solutions. For example, [PERSON] (2024) researched a dynamic target search for multiple UAVs based on cooperative coevolution motion-encoded particle swarm optimization [17]. [PERSON] (2021) proposed a cooperative search algorithm based on improved particle swarm optimization decisions for UAV swarms [18]. The ABC algorithm is a meta-heuristic optimization algorithm that solves complex optimization problems by mimicking the honey-harvesting behavior of a colony of bees. For instance, [PERSON] (2022) studied an improved artificial bee colony algorithm based on multi-strategy synthesis for UAV path planning [19]. [PERSON] (2021) researched a multi-UAV optimal mission assignment and path planning method for disaster rescue using an adaptive genetic algorithm and an improved artificial bee colony method [20]. [PERSON] (2019) implemented the artificial bee colony algorithm to solve UAV localization problems [21]. [PERSON] (2019) proposed a post-disaster UAV base station deployment method based on an artificial bee colony algorithm [22]. However, to achieve wide-area forest fire detection, UAV swarms require more than rational path planning. Forest fires often occur in high-temperature and arid wilderness forest areas, where fires spread rapidly and transportation and communication conditions are limited. UAV swarms need to prioritize the establishment of a good communication network to achieve formation flying and then accurately reach the fire point based on path planning algorithms [23; 24; 25]. Additionally, there is usually more than one fire point, so UAV swarms need to achieve large-scale, continuous, multi-target flight planning [26].