@inproceedings{61a142fbf80c4d3d9a8f824ffddfc689,
title = "Swarm Decision-Making via Evolutionary Optimization in Pursue-Containment Scenarios",
abstract = "Swarm systems require effective decision-making for timely action coordination. The pursuit-containment task, in particular, highlights the necessity of both intra-swarm co-operation and external competition. However, existing learning-based methods often suffer from poor coordination and are typically limited to small-scale swarms. To address these issues, this paper proposes an evolutionary optimization framework that efficiently facilitates real-time decision making for large-scale swarms, enabling the formation of a uniformly distributed containment circle around targets.",
keywords = "Evolutionary Optimization, Pursue-Containment, Swarm System",
author = "Xiaoyue Jin and Yinglan Feng and Ran Cheng and Dengxiu Yu and Tan, \{Kay Chen\}",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2025 International Conference on Machine Intelligence and Nature-Inspired Computing, MIND 2025 ; Conference date: 31-10-2025 Through 02-11-2025",
year = "2025",
doi = "10.1109/MIND67540.2025.11351877",
language = "英语",
series = "Proceedings of the 2025 International Conference on Machine Intelligence and Nature-Inspired Computing, MIND 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "187--188",
booktitle = "Proceedings of the 2025 International Conference on Machine Intelligence and Nature-Inspired Computing, MIND 2025",
}