## 2 Related Work  
The MCMUAVCTAP has garnered significant attention from researchers worldwide, resulting in numerous notable advancements. Reference [8] introduced a Clone Selection Algorithm (CSA) designed to optimize four objectives in multi-UAV task allocation: maximizing the number of successfully allocated tasks, maximizing the benefits of executing tasks, minimizing resource costs, and minimizing time costs. Experimental results demonstrated that CSA outperforms the genetic algorithm in multi-objective UAV task allocation. Reference [9] proposed three complementary algorithm variants--Allocation Loop (AL), Sorting and Allocation Loop (SAL), and Limit and Allocation Loop (LAL)--to enhance task assignment performance. Reference [10] developed an Improved Genetic Algorithm (IGA) featuring a logic-based unlocking mechanism for crossover and mutation operations. Simulations indicated that IGA effectively addresses the MCMUAVCTAP. Reference [11] applied dynamic decision theory (DT) to establish a multi-objective task assignment optimization model and introduced an Adaptive Weighted Particle Swarm Optimization (AWPSO) algorithm to solve it. The algorithm's environmental change detection and response strategy ensures population convergence and diversity. Experimental results confirmed the algorithm's strong convergence, diversity, and high decision quality. Reference [12] proposed an improved Wolf Pack Algorithm (WPA), which intelligently modulates the randomness in the traditional wolf pack search to solve the MCMUAVCTAP. Reference [13] developed a dynamic task assignment method for multi-UAVs using a combination of ant colony, bat, and gray wolf algorithms, which significantly reduces computation time and shortens UAV search duration and distance. [PERSON] [14] introduced an unbalanced target allocation model for multi-UAVs, based on situational assessment, solved using a Hungarian fusion genetic algorithm. [PERSON] [15] proposed an improved distributed Ant Colony Optimization (ACO) algorithm incorporating Q-learning to generate task sequences for each UAV. The algorithm uses a colony disorientation strategy to expand the search range and a search transition strategy to prevent premature convergence. Simulation experiments validated the practicality and reliability of this approach. Finally, [PERSON] [16] presented a Competitive Learning Pigeon-Inspired Optimization (CLPIO) algorithm for cooperative dynamic combat problems, integrating distributed swarm motion and centralized target allocation. The algorithm also includes a threshold trigger strategy to switch between sub-tasks, and a comparative analysis demonstrated its superior performance in solving mixed Nash equilibrium problems.  
Given that the SOA offers unique advantages over other heuristic algorithms in terms of adaptivity and parallelism, [PERSON] [17] proposed an optimal tidal current calculation method based on the inverse variance SOA. This method balances global search and local development capabilities, addressing the issues of poor global search ability and slow convergence speed in SOA. [PERSON] [18] introduced an improved SOA, incorporating a natural selection mechanism to expedite the search for optimal solutions. Additionally, it combines the unequal division method to solve dynamic optimization problems, with its feasibility demonstrated through a chemical dynamic optimization example. [PERSON] [19] presented a new Evolutionary Multi-objective Seagull Optimization Algorithm (EMoSOA), which produced highly convergent Pareto optimal solutions in engineering design problems. [PERSON] [20] proposed an initialization method based on Gaussian distribution and a Gaussian distribution-based dimensional sequential variation operator. By integrating non-uniform control vector parameterization with the improved SOA, simulation results indicated that the algorithm achieves similar or even higher solution accuracy.