Swarm intelligence (SI) is a collective behavior exhibited by decentralized, self-organized systems, typically inspired by natural phenomena such as ant colonies, bird flocks, bee swarms, or fish schools. It refers to the way these groups solve complex problems through simple, local interactions among individuals, without centralized control or a global plan.
In natural science, it is typically defined as a collective behavior that emerges from interaction between individuals in a group, whether they are animals or cells. In social science, it is defined as the intelligent behavior exhibited by groups of humans through their interactions and collaborations. In engineering applications, it refers to the use of algorithms and techniques that involve multiple intelligent agents collaborating, such as robots or sensor nodes, to compute autonomously.
Swarm intelligence demonstrates how simple rules and local interactions can lead to sophisticated and efficient solutions, making it a powerful concept for both understanding natural systems and designing artificial ones.
Our group mainly focuses on the following research directions:
- Distributed learning, optimization and control of multi-agent systems;
- Spatio-temporal restricted control of unmanned clusters;
- Modeling and decision of complex social systems.
