Integrating adaptive divide-and-conquer and large language model for scheduling large-scale tasks in electromagnetic satellite systems
- 14 March 2026
- journal article
- Published by Elsevier in Expert Systems with Applications
- Vol. 318
Abstract
No abstract availableFunding Information
- National Natural Science Foundation of China (72501042 72501214)
This publication has 46 references indexed in Scilit:
- Multi-objective early warning mission planning by multiple satellites using a critical task aggregation-based NSGA-II algorithmAdvances In Space Research, 2025
- Real-time traffic flow optimization using large language models and reinforcement learning for smart urban mobilityApplied Soft Computing, 2025
- A structured review of large language models in metaheuristic optimisationDecision Analytics Journal, 2025
- A neural priority model for agile earth observation satellite scheduling using deep reinforcement learningApplied Soft Computing, 2025
- LLMOA: A novel large language model assisted hyper-heuristic optimization algorithmAdvanced Engineering Informatics, 2025
- Deep reinforcement learning for Agile Earth Observation Satellites scheduling problem with variable image durationApplied Soft Computing, 2024
- Cross-calibration on the electromagnetic field detection payloads of the China Seismo-Electromagnetic SatelliteScience China Technological Sciences, 2022
- Evolutionary Computation for Large-scale Multi-objective Optimization: A Decade of ProgressesInternational Journal of Automation and Computing, 2021
- An improved adaptive large neighborhood search algorithm for multiple agile satellites schedulingComputers & Operations Research, 2018
- An adaptive large neighborhood search metaheuristic for agile satellite scheduling with time-dependent transition timeComputers & Operations Research, 2017