Improved Adaptive Large Neighborhood Search Algorithm Based on the Two-Stage Framework for Scheduling Multiple Super-Agile Satellites
Bibliographic record
Abstract
Super-agile satellites are high-performance earth observation satellites with active push-brooming capability and a real-time attitude control system. The highly flexible attitude maneuver capability of super-agile satellites has aggravated the complexity of the observation schedule. To solve the multiple super-agile satellite cooperative scheduling problem, we propose an improved adaptive large neighborhood search algorithm based on a two-stage framework (IALNS-TSF). The IALNS-TSF consists of two stages: the task allocation stage and the task sequencing stage. During the task allocation stage, the multiple super-agile satellite scheduling problem is decomposed into multiple single-satellite scheduling problems by heuristic allocation strategies. During the task sequencing stage, the scheduling plan is optimized through the improved adaptive large neighborhood search algorithm integrated with especially designed destroy and repair operations. The weights of the destroy and repair operators are updated through an adaptive mechanism, where the Metropolis acceptance criterion is employed to control the updates of solutions. Finally, to validate the effectiveness of the proposed method, extensive simulation experiments are conducted. The proposed method is compared with the improved simulated annealing algorithm based on a random insertion strategy, an adaptive large neighborhood search algorithm, and a variable neighborhood search algorithm. Experimental results demonstrate that the IALNS-TSF can obtain higher quality solutions in fewer iterations under different task scales and quantities of resources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".