Swarms and Network Intelligence

Edited by
June 2023
234 pages
  • ISBN978-3-0365-7920-7 (Hardback)
  • ISBN978-3-0365-7921-4 (PDF)

This book is a reprint of the Special Issue Swarms and Network Intelligence that was published in

Chemistry & Materials Science
Computer Science & Mathematics
Physical Sciences

This reprint covers a wide range of topics related to collective intelligence, exploring the interplay between swarm intelligence, network intelligence, and other emerging technologies. The first set of chapters focuses on the behavior and mechanisms of swarming. One chapter describes a locust-inspired model of collective marching on rings, while another demonstrates the experimental validation of entropy-driven swarm exploration under sparsity constraints using sparse Bayesian learning. These studies provide new insights into the principles of swarming and its potential applications in fields such as robotics and mobile crowdsensing. The next set of chapters discusses the integration of swarm intelligence with other emerging technologies such as deep learning and graph theory. These studies show how swarm intelligence can be combined with other advanced technologies to solve complex problems and improve decision-making processes. The reprint also covers the topic of network intelligence, including the study of social network analysis, Twitter user activity, and crowd-sourced financial predictions. These studies provide insights into how network intelligence can be harnessed to understand social dynamics and improve decision-making processes in various domains. The reprint concludes with a chapter that proposes a generative design approach for the efficient mathematical modeling of complex systems.

  • Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
generative design; automated learning; evolutionary learning; co-design; genetic programming; human behavior; socioeconomic status; data analysis; social media; crowd-sourcing; wisdom of the crowd; social learning; Bayesian models; risk; Docker Swarm; leader election; privilege escalation; defense evasion; cloud; collective intelligence; crowdsourcing; policymaking; public policy; e-participation; literature review; deep learning; cybersecurity; artificial intelligence; swarm intelligence; adversarial AI; information theory; entropy; models; neural networks; communication; multi-agent; deep reinforcement learning; partial observability; distributed estimation; Sparse Bayesian Learning; exploration; swarm; multi-agent systems; consensus; D-optimal design; mobile crowdsensing; deep reinforcement learning; UAV control; graph network; maximum-entropy learning; mobile robotics; swarms; crowd dynamics; natural algorithms; locusts; n/a