Swarm Intelligence

The collective behavior of decentralized, self-organized systems, natural or artificial.

How it works

Swarm intelligence algorithms draw inspiration from the collective behaviour of biological systems: ant colonies find shortest paths using pheromone trails (Ant Colony Optimisation), bird flocks maintain formation via simple local rules, and bee swarms optimise search through waggle dance communication. In AI, Particle Swarm Optimisation (PSO) uses a population of candidate solutions that move through the search space guided by their personal best and the swarm's global best. No central coordinator is required.

Why it matters

Swarm intelligence solves distributed optimisation problems that are intractable for centralised algorithms, such as robotic swarms coordinating logistics, adaptive network routing, and distributed sensor coverage. It also provides a metaphor for multi-agent AI systems: a collection of simple agents following local rules can collectively solve problems no single agent could. This insight is increasingly relevant as agent swarms — like those in Kimi K2.6 — tackle complex tasks through parallelised specialisation.