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arXiv AI By He Wang, Junyu Wu, Yeye Liu, Yifan Zhou, Jie Zhang, Hui Li, Yanjie Song, Liang Li

Implicit Q-learning-bootstrapped ant colony optimization for maritime moving-target observation scheduling with agile satellites

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The paper introduces IQACO, an implicit Q‑learning‑bootstrapped ant colony optimization algorithm for scheduling maritime moving‑target observations with agile Earth‑observation satellites. IQACO embeds an offline implicit Q‑learning module into the ant colony framework to adaptively adjust pheromone, heuristic, and evaporation parameters based on a compact search‑state representation. Experiments on 14 scenarios show IQACO outperforms conventional ant colony optimization, improving mean observation benefit by 3.40%–9.40%, accelerating convergence, and maintaining stability across different objective‑weight settings.

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