TY - GEN
T1 - Behavioral Entropy-Based Measure of Environmental Adaptability of Swarm Intelligence Systems
AU - Meng, Yangyang
AU - Yan, Bo
AU - Gong, Jian
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - The adaptive capability of swarm intelligence systems in dynamic environments is an important part of their overall intelligence, but quantitative assessment methods for this capability are still scarce. In order to establish an effective quantitative assessment method, the behavioral entropy method is introduced, illustrated with a typical UAV cluster radar jamming task. The assessment indexes are constructed from the frequency, energy, and spatial domains. A comparative simulation analysis of UAV cluster with fixed, random, semi-adaptive, and fully adaptive strategies is conducted. The results show that behavioral entropy can accurately portray the adaptive performance differences of UAV cluster strategies, which verifies the effectiveness of behavioral entropy in characterizing the environmental adaptability of swarm intelligence systems and provides an effective theoretical basis and technical support for strategy optimization of swarm intelligence systems.
AB - The adaptive capability of swarm intelligence systems in dynamic environments is an important part of their overall intelligence, but quantitative assessment methods for this capability are still scarce. In order to establish an effective quantitative assessment method, the behavioral entropy method is introduced, illustrated with a typical UAV cluster radar jamming task. The assessment indexes are constructed from the frequency, energy, and spatial domains. A comparative simulation analysis of UAV cluster with fixed, random, semi-adaptive, and fully adaptive strategies is conducted. The results show that behavioral entropy can accurately portray the adaptive performance differences of UAV cluster strategies, which verifies the effectiveness of behavioral entropy in characterizing the environmental adaptability of swarm intelligence systems and provides an effective theoretical basis and technical support for strategy optimization of swarm intelligence systems.
KW - behavioral entropy
KW - environmental adaptability
KW - quantitative assessment
KW - swarm intelligence
UR - https://www.scopus.com/pages/publications/105014317193
U2 - 10.1109/MLISE66443.2025.11100208
DO - 10.1109/MLISE66443.2025.11100208
M3 - Conference contribution
AN - SCOPUS:105014317193
T3 - 2025 5th International Conference on Machine Learning and Intelligent Systems Engineering, MLISE 2025
SP - 125
EP - 133
BT - 2025 5th International Conference on Machine Learning and Intelligent Systems Engineering, MLISE 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Machine Learning and Intelligent Systems Engineering, MLISE 2025
Y2 - 13 June 2025 through 15 June 2025
ER -