## 6 Discussion
### Future Research Trends  
MSIMP based on SM-EMOEAs has significant advantages in effectively dealing with high-dimensional complexity, multi-objective conflicts, and dynamic changes. However, MSIMP based on SM-EMOEAs still faces many challenges, so it is important to discuss the future directions and trends of this field.  
1. Distributed parallel computing combined with MSIMP based on SM-EMOEAs. There has recently been a sharp increase in the demand for observation data for multi-satellite imaging missions, and the training process of surrogate models in SM-EMOEAs involves a large amount of computational data. However, a single node has limited computational capabilities, which makes it difficult to satisfy the huge demand for data processing in a short time. Distributed parallel computing is capable of training surrogate models in a multi-node parallel environment, accelerating agent model training using a distributed deep learning framework and realizing the parallelized evaluation of MSIMP schemes. Thus, it can significantly improve the computational efficiency and processing power of SM-EMOEAs. Therefore, the investigation of efficient and robust distributed parallel computing is of great significance for the future development of MSIMP based on SM-EMOEAs.
2. Data-driven and big data analysis combined with MSIMP based on SM-EMOEAs. MSIMP involves mission requirements in a complex environment with multiple objectives, constraints, and dynamic variables, and there is a large number of uncertainties, such as weather variations and satellite orbital drift, in the implementation of the MSIMP scheme. In addition, SM-EMOEAs rely on high-precision surrogate models to evaluate the real objective function, which involves a large amount of observation data. Data-driven and big data analytics enable real-time MSIMP observation and the analysis of historical data, respond to the changing needs of MSIMP in complex environments, and improve the prediction accuracy and generalization of surrogate models. Therefore, the investigation of efficient and intelligent data-driven approaches and big data analysis are crucial for the future development of MSIMP based on SM-EMOEAs.
3. Integrating deep and reinforcement learning techniques with MSIMP based on SM-EMOEAs. Optimal MSIMP involves complex decision-making scenarios, such as disaster monitoring and emergency response, crop growth monitoring, urban traffic flows, and so on, and traditional optimization methods have limitations in dealing with high-dimensional complexity. Integrating deep learning with reinforcement learning can improve the optimization accuracy and efficiency through enabling the processing of high-dimensional data through deep neural networks and the capture of complex relationships in mission planning. In addition, an end-to-end learning framework that integrates deep learning with reinforcement learning can extract key features from satellite sensor data to guide MSIMP task allocation. Therefore, the integration of deep learning and reinforcement learning techniques with MSIMP based on SM-EMOEAs can provide powerful technical support and optimization capabilities for MSIMP.
4. Augmented reality (AR) and virtual reality (VR)-assisted decision-making combined with MSIMP based on SM-EMOEAs. MSIMP involves high-dimensional data and complex decision-making processes, and traditional decision support systems are limited in terms of interface interaction. AR and VR technologies provide immersive and interactive environments that enable the real-time visualization of the mission progress and satellite status, enabling users to intuitively manipulate and analyze mission planning data in a virtual space. Therefore, investigating the combination of AR- and VR-assisted decision making with MSIMP based on SM-EMOEAs will significantly advance the development of MSIMP visualization.