🐜 Stigmergy Swarm – Collective Intelligence Through Pheromone Trails¶

This simulation demonstrates stigmergy: agents that communicate indirectly through modifications of their shared environment.

Ant-like agents search for food on a 2D grid. When an agent finds food it carries a unit back to the nest, depositing pheromone along the way. Other agents are probabilistically attracted toward higher pheromone concentrations – so successful paths get reinforced automatically.

No agent has any global knowledge. Yet over time the swarm converges on efficient routes from nest to food.

🧠 Key Concepts¶

Stigmergy – indirect coordination via environmental traces
Self-organization – global structure from local rules
Positive feedback – successful paths attract more traffic
Evaporation – unused paths decay, preventing lock-in

🖼 Visualisation¶

The matplotlib window shows:

Background heatmap – pheromone concentration (log-scaled)
Green diamonds – active food sources
Blue square – nest
White dots – searching agents
Red dots – agents carrying food back to nest

Press ESC to stop the simulation.

▶ Run¶

cd simulation-models/stigmergy-swarm
python3 stigmergy_swarm.py