Evolutionary Optimization for Satellite Constellation Task Scheduling Based on Intelligent Optimization Engine

DU Yonghao; LI Lei; XU Shilong; CHEN Ming; CHEN Yingguo

doi:10.11999/JEIT240974

Volume 47 Issue 6

Jun. 2025

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Article Navigation > Journal of Electronics & Information Technology > 2025 > 47(6): 1645-1657

DU Yonghao, LI Lei, XU Shilong, CHEN Ming, CHEN Yingguo. Evolutionary Optimization for Satellite Constellation Task Scheduling Based on Intelligent Optimization Engine[J]. Journal of Electronics & Information Technology, 2025, 47(6): 1645-1657. doi: 10.11999/JEIT240974

Citation:

DU Yonghao, LI Lei, XU Shilong, CHEN Ming, CHEN Yingguo. Evolutionary Optimization for Satellite Constellation Task Scheduling Based on Intelligent Optimization Engine[J]. Journal of Electronics & Information Technology, 2025, 47(6): 1645-1657. doi: 10.11999/JEIT240974

Citation:

DU Yonghao, LI Lei, XU Shilong, CHEN Ming, CHEN Yingguo. Evolutionary Optimization for Satellite Constellation Task Scheduling Based on Intelligent Optimization Engine[J]. Journal of Electronics & Information Technology, 2025, 47(6): 1645-1657. doi: 10.11999/JEIT240974

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Evolutionary Optimization for Satellite Constellation Task Scheduling Based on Intelligent Optimization Engine

doi: 10.11999/JEIT240974 cstr: 32379.14.JEIT240974

College of Systems Engineering, National University of Defense Technology, Changsha 410073, China

Received Date: 2024-10-30
Rev Recd Date: 2025-04-28

Available Online: 2025-05-12

Publish Date: 2025-06-30

Abstract

Abstract

Objective The expansion of China’s aerospace capabilities has led to the widespread deployment of remote sensing satellites for applications such as land resource surveys and disaster monitoring. However, current methods face substantial challenges in the integrated scheduling of complex targets, including multi-frequency observations, dense point clusters, and wide-area imaging. This study develops an intelligent task planning engine architecture tailored for heterogeneous satellite constellations. By applying advanced modeling and evolutionary optimization techniques, the proposed framework addresses the collaborative scheduling of multi-dimensional targets, aiming to overcome key limitations in traditional satellite mission planning. Methods Through systematic analysis of models and algorithms, this study decouples the “Constraint-Decision-Reward” framework and develops an optimization algorithm module featuring “global evolution + local search + data-driven” strategies. At the modeling level, standard tasks are derived via target decomposition, and a multi-dimensional scheduling model for complex targets is established. At the algorithmic level, a Learning Memetic Algorithm (LMA) based on dual-model evolution is proposed. This approach incorporates strategies for initial solution generation, global optimization, and a generalized neighborhood search operator template to improve solution diversity and enhance global exploration capabilities. Additionally, data-driven optimization and dynamic multi-stage rapid insertion strategies are introduced to address real-time scheduling requirements. Results and Discussions Comprehensive experimental comparisons are conducted across three scenario scales—low, medium, and high difficulty—and three task planning scenarios (static scheduling, dynamic three-stage scheduling, and dynamic twelve-stage scheduling). Both classical and advanced algorithms are evaluated. Ablation experiments (Tables 4 and 5) assess the contribution of each component within the LMA. In all task scenarios, the proposed method consistently outperforms advanced algorithms, including adaptive large neighborhood search and the reinforcement learning genetic algorithm, as shown in Figure 11 and Table 3. The algorithm reliably completes iterations within 20 seconds, demonstrating high computational efficiency. Conclusions By standardizing complex targets and generating tasks, this research effectively addresses the integrated scheduling challenge of multi-dimensional objectives across heterogeneous resources. Experimental results show that the LMA outperforms traditional algorithms in terms of both solution quality and computational efficiency. The dual-model evolution mechanism enhances the algorithm’s global search capabilities, while the dynamic insertion strategy effectively handles scenarios with dynamically arriving tasks. These innovations highlight the algorithm’s significant advantages in aerospace mission scheduling.

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