Emergent Sorting in Networks of Router Agents
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
An Immuno-engineering Approach for Anomaly Detection in Swarm Robotics
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
Interfaces
Acoustic counting algorithms for wireless sensor networks
Proceedings of the 6th ACM symposium on Performance evaluation of wireless ad hoc, sensor, and ubiquitous networks
Multi-policy optimization in self-organizing systems
SOAR'09 Proceedings of the First international conference on Self-organizing architectures
Fast forward RBF network construction based on particle swarm optimization
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part II
The application of swarm intelligence in service-oriented product lines
Proceedings of the 15th International Software Product Line Conference, Volume 2
BeesAnts: a new nature-inspired routing algorithm
International Journal of Communication Networks and Distributed Systems
Ant algorithms for image feature extraction
Expert Systems with Applications: An International Journal
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The laws that govern the collective behavior of social insects, flocks of birds, or fish schools continue to mesmerize researchers. While individuals are rather unsophisticated, in cooperation they can solve complex tasks, a prime example being the ability of ant colonies to find shortest paths between their nests and food sources. Task-solving results from self-organization, which often evolves from simple means of communication, either directly or indirectly via changing the environment, the latter referred to as stigmergy. Scientists have applied these principles in new approaches, for example to optimization and the control of robots. Characteristics of the resulting systems include robustness and flexibility. This field of research is now referred to as swarm intelligence. The contributing authors are among the top researchers in their domain. The book is intended to provide an overview of swarm intelligence to novices, and to offer researchers in the field an update on interesting recent developments. Introductory chapters deal with the biological foundations, optimization, swarm robotics, and applications in new-generation telecommunication networks, while the second part contains chapters on more specific topics of swarm intelligence research such as the evolution of robot behavior, the use of particle swarms for dynamic optimization, and organic computing.