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

自20世纪90年代初以来，社会科学和生物科学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，人们观察到，虽然动物群体中的个体主体不具备复杂行为的能力，但多个主体之间的联合协调导致了规律行为模式的表现。已经开发了几种算法来模拟动物群的运动。这些研究在建模和理解复杂现象以及在从生物学到纳米技术等领域开发应用方面被证明是有用的。该研究包括调查生物和社会科学中群体智能研究与最近系统理论中自适应网络研究之间的相互联系。自适应网络由分布在地理区域内的各向同性节点组成。节点感知环境，并尝试基于它们嘈杂的观察来理解感兴趣的现象，而没有任何节点充当中心控制角色。节点通过本地交互相互协作，并根据节点收集的数据和从其邻居接收的数据调整其状态和网络拓扑。到达节点的信息通过扩散过程在整个网络中传播。与非合作网络相比，信息的扩散产生了一种集体智慧，这一点可以从学习和收敛行为的改善中得到证明。对群体行为和自适应网络动力学的深入研究可以为设计自适应网络和理解群体行为提供替代技术，并对这些发展可能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.