## 3 Description of MSIMP
### MSIMP Based on Traditional Optimization Algorithms  
MSIMP based on traditional optimization algorithms is mainly used to solve problems such as maximizing the task benefit, maximizing the task assignment rate, and minimizing the response time. The traditional optimization algorithms commonly used at present can be divided into two categories: deterministic algorithms and heuristic algorithms. Deterministic algorithms mainly include linear programming [52], integer programming [53], mixed integer programming [54], dynamic programming [55], the branch and bound method [56], and the column generation method [57]. Heuristic algorithms mainly include the simulated annealing algorithm [58], genetic algorithm [59], particle swarm algorithm [60], ant colony algorithm [61], forbidden search [62], and differential evolutionary algorithm [63]. The application scenarios for each algorithm are specifically detailed in Table 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm Type** & **Algorithm** &
\begin{tabular}{c} **Computation Costs** \\ _/Response Time_ \\ \end{tabular} & \multicolumn{1}{c}{**Application Scenario/Algorithm Feature**} \\ \hline \multirow{8}{*}{Deterministic algorithm} & Linear programming & Low/Fast & It solves optimization problems under linear constraints, such as maximizing imaging coverage and minimizing imaging time. \\  & (LP) & Moderate/Moderate & It solves task allocation problems that require integer solutions and ensures the rationality of task and resource allocation. \\ \cline{2-4}  & Mixed integer programming & High/Slow & It is used to solve complex programming problems with both continuous and discrete variables. \\  & (MIP) & & \\ \cline{2-4}  & Dynamic programming & High/Slow & It is used for phased decision problems to ensure that the optimal decision at each stage can find the global optimal solution. \\  & (DP) & High/Moderate & It is used for integer and combinatorial optimization problems to find the optimal solution by systematically dividing search space. \\ \cline{2-4}  & Branch and bound & & \\ (B&R)B & \multirow{2}{*}{High/Moderate} & It is used for large-scale integer programming and mixed integer programming, optimized by decomposing the problem and gradually generating solution columns. \\ \cline{2-4}  & Column generation & Moderate/Fast & It is used for large-scale integer programming and mixed integer programming, optimized by decomposing the problem and gradually generating solution columns. \\ \cline{2-4}  & Simulated annealing & Moderate/Moderate & It solves global optimization in search space and achieves optimization from global to local by gradually reducing randomness. \\ \cline{2-4}  & Genetic algorithm & & \\ (GA) & & & \\ \cline{2-4}  & Particle swarm & & \\  Heuristic algorithm & optimization & & \\ \cline{2-4}  & (PSO) & & \\ \cline{2-4}  & Ant colony algorithm & High/Moderate & It is used in discrete optimization problems to find the optimal task assignment method by simulating ant foraging behavior. \\ \cline{2-4}  & & Tabu search & \\ (TS) & & & \\ \hline \multicolumn{4}{c}{} \\ \multicolumn{4}{c}{} \\ \multicolumn{4}{c}{} \multicolumn{4}{c}{} \\ \multicolumn{4}{c}{} \multicolumn{4}{c}{} \\ \multicolumn{4}{c}{} \multicolumn{4}{c}{} \\ \multicolumn{4}{c}{} \multicolumn{4}{c}{} \\ \multicolumn{4}{c}{} \multicolumn{4}{c}{} \\ \multicolumn{4}{c}{} \multicolumn{4}{c}{} \\ \multicolumn{4}{c}{} \multicolumn{4}{c}{} \\ \multicolumn{4}{c}{} \multicolumn{4}{c}{} \\ \multicolumn{4}{c}{} \multicolumn{4}{c}{} \\ \multicolumn{4}{c}{} \\ \multicolumn{4}{c}{} \multicolumn{4}{c}{} \\ \multicolumn{4}{c}{} \\ \multicolumn{4}{c}{} \multicolumn{4}{c}{} \\ \multicolumn{4}{c}{} \\ \multicolumn{4}{c}{} \end{tabular}
\end{table} 
Table 1: Applications of MSIMP based on traditional optimization algorithms. The above traditional optimization algorithms have many advantages, such as strong global search capabilities, the wide coverage area of the solution space, and so on, and can effectively deal with a variety of optimization objective functions and constraints.