A Go-based ant colony simulation that demonstrates emergent behavior and swarm intelligence through various scenarios. The simulation showcases how simple rules followed by individual ants can lead to complex, colony-level behaviors. This is a fun project to practice Golang and experiment with Raylib bindings.

Results

• Multiple simulation scenarios:
  • home: Basic home navigation scenario
  • wandering: Ants wandering behavior demonstration
  • food: Food foraging simulation
  • pheromone: Pheromone-based path finding
  • one_nest: Single nest colony simulation
  • two_nests: Two competing colonies
  • four_nests: Four colony interaction
  • surrounding: Environmental interaction scenario

Examples

https://github.com/user-attachments/assets/74aef395-ea84-4dee-a297-f2055b4a29a7

https://github.com/user-attachments/assets/00ada0db-f23e-486d-8008-5a3420412990

https://github.com/user-attachments/assets/d97918a6-d355-412b-a8e5-57fd11360636

Prerequisites

• Go 1.20 or higher
• raylib-go - Golang bindings for raylib, see how to properly install it depending on your os here

Try it locally

1. Clone the repository:

git clone https://github.com/firasjaber/ant-sim.git
cd ant-sim

2. Install dependencies:

go mod download

3. Run a simulation scenario:

go run main.go [scenario_name]

For example:

go run main.go four_nests

Simulation Scenarios

• home: Demonstrates ants returning to their nest
• wandering: Shows random exploration behavior
• food: Simulates food foraging with pheromone trails
• pheromone: Focuses on pheromone-based communication
• one_nest: Single colony behavior
• two_nests: Competition between two colonies
• four_nests: Complex interactions between four colonies
• surrounding: Environmental factors affecting ant behavior

Built With

• Go
• raylib-go