@inproceedings{8f8ea23e099b44e39430191fef70f261,
title = "Learn to Cooperate: Reinforced Ant Colony Algorithm for Multi-Agent Rescue Missions",
abstract = "This paper introduces a novel solution to the multi-agent rescue problem, presenting the Reinforced Ant Colony Algorithm (RACA), which combines Learning Automata (LA) with Ant Colony Optimization (ACO) to enhance agent cooperation. Traditional ACO methods face challenges in multi-agent tasks due to limited path diversity and inefficient cooperation. RACA addresses these issues by dynamically selecting between individual and global pheromone updates, optimizing agent collaboration and task allocation. The algorithm incorporates dynamic heuristics and adaptive strategies, further enhancing its efficiency and adaptability, making it a promising solution for complex rescue scenarios. Validated in a Maritime Collaborative Rescue (MCR) model, where agents collaborate to rescue individuals on a grid map, experimental results demonstrate that RACA outperforms comparative algorithms, achieving higher rescue success rates.",
keywords = "ant colony optimization, multirobot system, reinforcement learning, search and rescue, task allocation",
author = "Zixu Li and Bin Xin and Qier Mu and Yuhui Huang",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2025 IEEE International Conference on Unmanned Systems, ICUS 2025 ; Conference date: 18-09-2025 Through 19-09-2025",
year = "2025",
doi = "10.1109/ICUS66297.2025.11294193",
language = "English",
series = "Proceedings of 2025 IEEE International Conference on Unmanned Systems, ICUS 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "117--123",
editor = "Rong Song",
booktitle = "Proceedings of 2025 IEEE International Conference on Unmanned Systems, ICUS 2025",
address = "United States",
}