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Ant Colony Simulation: The Power of Swarm Intelligence

CSEF · 2026 Applied Mechanics (Junior Division)

Daniel Delima

Overview

Have you ever noticed a flock of birds fly by or even a bees’ nest have numerous insects or animals work together to help each other survive? Did you wonder how they coordinated off instinct, even without a leader? My project attempts to explore the fascinating phenomenon of swarm intelligence, specifically how ants can coordinate efficiently to complete complex tasks without traditional communication or centralized leadership. I wanted to prove that these rudimentary artificial ants using only local information and simple logic could model complex ant colony foraging behaviors. My stated hypothesis is that even from my basic simulation emergent behaviors would arise. To prove this, I used the game engine Godot, in a language called GDScript. I took much inspiration from a video by Sebastian Lague on an ant colony simulation, and made improvements and used less ants. The main independent variable was the environmental layout, which was randomly generated using a Perlin Noise algorithm. The locations of the nest and food were placed to create unique challenges for the ants. The dependent variables were how quickly the ants established a path and how efficient it was. The first experiment shows how the colony adapted to a dynamic environment. Ants that strayed away from the established route occasionally found new paths. Shortcuts were then reinforced by the swarm. This eventually led the colony to find the most efficient route even in a dynamic environment. The second experiment shows how the ants streamlined their routes by “hugging” turns. The before and after shows how the path became simpler and straighter as the simulation progressed. In conclusion, my experiments proved my hypothesis correct. Complex and emergent behavior could come from rudimentary ants. These results show how swarm intelligence is a powerful phenomenon with many real world applications.

Competition history

  • CSEF 2026 Applied Mechanics (Junior Division) · Entry J-02-18

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