ants simulation

This is great to see. Years ago I created a basic simulation using the same principles of pheromone gradients, one for the nest and one for food. ▶ this video It a fascinating example of emergent behavior from simple rules. I've been wanting to find time to revisit the concept and produce an updated version, your simulation seems to include everything I wanted to do and more.

Take a look at this visualization ▶ this video There's a repo in the description. Might not be specifically relevant to your project though

I loved reading this because I made something exceedingly similar a few years ago, and I'm similarly fascinated with how complex behavior can emerge from such simple rules. ▶ this video My ants also had no conception of where their home was located, their behavior is entirely pheromone driven. They can "read" pheromones from any adjacent cell. They leave a pheromone every movement depending on which state they are in, either "find food" or "return food". In my system, the pheromones have a strength. Whenever an ant touches the nest, his "nest pheromone" is at full strength (255). Each movement decreases the strength by 1. This sets up a natural gradient of nest strength for ants to follow. They also "deteriorate" each round as yours do. A similar setup is used with food, such that a high food scent is created when they pick up food, and decreases in strength as they return to the nest. So to find food, they try to move up a food gradient or down the nest gradient (away from the nest), while to return food they move up the nest gradient (towards the nest). Their searching consists of a random walking into cells that have no nest scent, or following a food trail. They have an energy cost for moving, and if it's too low they have to eat a piece of food. Returning a piece of food to the hill spawns a new ant. The lines on the right side show the population of each colony and the total food on the board. From these extremely basic rules, they are very efficient at clearing the "world"