Zhang Junqiao, Qu Qiang, Chen Xuebo
School of Electronics and Information Engineering, University of Science and Technology Liaoning, Anshan, 114051, China.
Sci Rep. 2025 Jan 29;15(1):3709. doi: 10.1038/s41598-025-88440-3.
Collective behavior in biological systems emerges from local interactions among individuals, enabling groups to adapt to dynamic environments. Traditional modeling approaches, such as bottom-up and top-down models, have limitations in accurately representing these complex interactions. We propose a novel potential field mechanism that integrates local interactions and environmental influences to explain collective behavior. This study introduces dynamic potential fields, where individuals perceive and respond to local potential fields generated by environmental cues and other individuals. We develop a mathematical framework combining distributed learning and swarm control to simulate and analyze collective behavior under varying conditions. Our simulations span a variety of environmental conditions, including standard environments where organisms interact under typical conditions, high noise environments where interactions are disrupted by random fluctuations, high density environments with increased competition for space, high risk environments featuring areas of strong negative potential field, and multiple resource environments with varying degrees of resource availability. These simulations demonstrate the adaptability and resilience of biological groups to changing and challenging conditions. Results reveal how potential fields facilitate the emergence of stable and coordinated behaviors, providing insights into self-organization, cooperation, and competition in nature. This framework enhances our understanding of collective behavior and has implications for bio-robotics, distributed systems, and complex networks.
生物系统中的集体行为源于个体之间的局部相互作用，使群体能够适应动态环境。传统的建模方法，如自下而上和自上而下的模型，在准确表示这些复杂相互作用方面存在局限性。我们提出了一种新颖的势场机制，该机制整合了局部相互作用和环境影响来解释集体行为。本研究引入了动态势场，个体在其中感知并响应由环境线索和其他个体产生的局部势场。我们开发了一个结合分布式学习和群体控制的数学框架，以模拟和分析不同条件下的集体行为。我们的模拟涵盖了各种环境条件，包括生物体在典型条件下相互作用的标准环境、相互作用因随机波动而中断的高噪声环境、空间竞争加剧的高密度环境、具有强负势场区域的高风险环境以及资源可用性不同程度的多资源环境。这些模拟展示了生物群体对变化和具有挑战性条件的适应性和恢复力。结果揭示了势场如何促进稳定和协调行为的出现，为自然界中的自组织、合作和竞争提供了见解。这个框架增强了我们对集体行为的理解，并对生物机器人学、分布式系统和复杂网络具有启示意义。
