## 1 Introduction
[9] established the target optimization mathematical model according to the weighted sum of UAV distance fuel consumption and the latest task completion time. The allocation results fully considered the actual work of the UAV carrier and the completion advantage of the attack, reconnaissance, warnings, and other tasks, but they did not start from the perspective of the carrier's actual task goal; therefore, the model did not achieve optimal comprehensive efficiency for the task. [PERSON] et al. [10] established a fire allocation model based on the minimum waste of fire, which took into account the target damage requirements and the oversaturation problem of fire allocation. Their model enhanced the rationality of the allocation. [PERSON] et al. [11] deeply studied the target task allocation optimization model under the background of specified tasks, but they only considered the allocation of a single task or a single target, and did not consider the workload-balancing ability of heterogeneous multi-load and multi-target platforms in collaborative action from the perspective of the comprehensive performance indicators of the actual surface platform cluster. The above scholars have established models based on the requirements of a single target under the background of a specific task, but the problem of the unified allocation of the multi-task targets involved in cross-domain collaboration has not been solved so far. After decades of development, multiple research branches on solving algorithms for task allocation models have been formed and applied in various fields of engineering technology. Since [PERSON]'s work [12] in ballistic missile defense strategy optimization research in 1958, the proposed missile allocation problem (MAP) and the weapon target allocation (WTA) problem have been widely considered typical NP-complete combinatorial optimization problems [13]. After decades of development, the research [14] on the WTA problem can be divided into static WTAs [15; 16] and dynamic WTAs [17], and into centralized WTAsand distributed WTAs [18; 19] in terms of decision-making power. In terms of modeling methods, they can be divided into traditional algorithms, such as integer programming, dynamic programming [20], and graph theory, and innovative algorithms, such as game theory [21] and multi-agent theory. In terms of optimization methods, they can be divided into traditional exact algorithms, heuristic algorithms, and hybrid intelligent algorithms that combine multiple intelligent algorithms. The existing task allocation algorithms are summarized in Figure 1 below. The traditional algorithms have high accuracy but experience difficulty overcoming the real-time requirements of large-scale task allocation problems. Intelligent algorithms such as genetic algorithms and heuristic algorithms have been widely used to simplify the problem scale and the time complexity of solutions at the cost of a certain level of accuracy. At present, various intelligent optimization algorithms, such as the particle swarm optimization algorithm [22], the ant colony algorithm [23], and the chimpanzee algorithm [24], have difficulty solving the intractable problem of cross-domain collaborative task allocation with complex constraints, a huge scale, and high timeliness requirements. The dung beetle optimizer (DBO) is a new metaheuristic swarm intelligence search algorithm proposed by [PERSON] et al. [25] that imitates the survival behaviors of dung beetles, such as ball bowling, dancing, foraging, stealing, and reproduction, as its basic principle. It has attracted attention due to its strong evolutionary ability, fast convergence speed, and strong optimization ability [26; 27]. It has been successfully applied to path planning [28], underwater wireless sensor network (UWSN) coverage [29], and feature selection [30].