CIF: SMALL: Explorations and Insights into Adaptive Networks, Animal Flocking Behavior, and Swarm Intelligence

摘要自20世纪90年代初以来，在社会科学和生物科学领域，在动物群体行为和群体智能的研究中，出现了一些有用的优化算法。例如，已经观察到，虽然动物群体中的个体代理不能进行复杂的行为，但多个代理之间的联合协调导致行为的规则模式的表现。已经开发了几种算法来模拟动物群的运动。这些研究被证明是有用的建模和理解复杂的现象，并在开发应用领域，从生物学到纳米技术。该研究涉及调查生物和社会科学中的群体智能研究与系统理论中自适应网络的最新研究之间的相互联系。 自适应网络由分布在地理区域上的各向同性节点组成。节点感知环境，并试图根据它们的噪声观测来理解感兴趣的现象，而没有任何节点扮演中央控制角色。节点通过本地交互相互协作，并响应于在节点处收集的数据和从其邻居接收的数据来调整它们的状态和网络拓扑。到达节点的信息通过扩散过程在整个网络中传播。信息的扩散导致了集体智慧的形成，这可以通过相对于非合作网络的改进的学习和收敛行为来证明。对群体行为和自适应网络的动力学进行更深入的研究，可以为设计自适应网络和理解群体行为提出替代技术，并对这些发展所激发的应用程序产生潜在影响。

Abstract Since the early 1990s, some useful optimization algorithms have emerged from the social and biological sciences in their studies of animal flock behavior and swarm intelligence. It has been observed, for example, that while individual agents in an animal colony are not capable of complex behavior, the combined coordination among multiple agents leads to the manifestation of regular patterns of behavior. Several algorithms have been developed to model the movement of animal flocks. These investigations are proving useful in modeling and understanding complex phenomena and in developing applications in areas ranging from biology to nanotechnology. The research involves investigating interconnections between these studies on swarm intelligence in the biological and social sciences, and more recent studies on adaptive networks in system theory. Adaptive networks consist of isotropic nodes spread over a geographic domain. The nodes sense the environment and attempt to understand a phenomenon of interest based on their noisy observations and without any node taking a central control role. The nodes cooperate with each other through local interactions and adapt their states, and the network topology, in response to data collected at the nodes and data received from their neighbors. Information arriving at a node propagates throughout the network by means of a diffusive process. The diffusion of information results in a form of collective intelligence as is evidenced by improved learning and convergence behavior relative to non-cooperative networks. A closer study of the dynamics of swarm behavior and adaptive networks can suggest alternative techniques for designing adaptive networks and for understanding flock behavior, with potential impact on the applications that can be motivated from these developments.