## 1 Introduction
[34] proposed a mixed-integer programming algorithm to solve MSIMPLTS by maximizing the objective function, considering factors such as satellite resource consumption and user request priority in large-scale scenarios. [PERSON] et al. [35] proposed a local search algorithm to solve the target task planning problem in large-scale scenarios, and its efficient constraint handling mechanism improves the algorithm's efficiency. [PERSON] et al. [36] utilized the overall benefits of an optimized task, antenna load balancing, and task completion timelines simultaneously and proposed a multi-objective differential evolutionary algorithm based on spatial partitioning and an adaptive selection strategy to solve the difficult problem of inefficient allocation in large-scale scenarios. [PERSON] et al. [37] proposed heuristic algorithms based on an edge computing framework to ensure that satellite missions in large-scale scenarios can be optimized in terms of resource utilization. [PERSON]'s [38] deep reinforcement learning procedure trained encoder-decoder-structured neural networks (NNs) to achieve simultaneous optimization of the failure rate and response time of a multi-satellite imaging mission planning system in large-scale scenarios. Most of the models and algorithms proposed in the current research belong to the holistic optimization approach, which significantly improves the planning ability; however, it also brings about an increase in the computational complexity and an extension of the planning time. In addition, allocation conflicts among multiple tasks make the optimal allocation of resources difficult to achieve. The specific difficulties are as follows:  
* Addressing the MSIMPLTS problem requires considering multiple optimization objectives, such as the target task benefit, task allocation rate, and response time. Most of the current studies use single-objective optimization models to achieve the optimal solution by setting the weights of different objectives. However, conflict or mutual constraints between the optimization objectives make it difficult to obtain high-quality task-planning solutions. In this context, designing optimization models that can take into account the interactions and constraints between the objectives is a new challenge in solving the MSIMPLTS problem. * Existing research on the MSIMPLTS problem mainly focuses on the study of the sequence of satellite-target mission assignments, ignoring the feature that the same satellite resource can observe multiple targets at the same time. This leads to difficulties such as overlapping observation time windows, conflicting planning programs, and low utilization of satellite resources. Therefore, constructing a more reasonable mission planning method is a major objective for MSIMPLTS work. * Large-scale target task requirements lead to a significant increase in the problem decision dimension and a larger solution space. The current MSIMPLTS-solving algorithms mostly focus on improving the ratio of feasible solutions, which leads to problems such as the mission planning scheme easily falling into local optimums or converging prematurely. Going forward, designing reasonable solution algorithms that effectively balance the solution efficiency and solution quality is also another challenge for MSIMPLTS studies. To overcome these difficulties, this work takes imaging satellites as the research object and proposes a progressive optimization mechanism and population size adaptive strategy for an improved differential evolution algorithm in large-scale target scenarios. The main contributions of this work are as follows:  
* In terms of constructing a task planning model, we propose the MSIMPLTS based on Multi-layer Objective Optimization (MSIMPLTS-MLOO). The model is structured by setting up three layers: the superstructure, mesostructure, and undersructure, with each layer focusing on a different optimization objective. These, respectively, optimize the task benefit, task completion, and response time functions.