Ant Optimization in NetLogo By: Stephen Johnson

Optimization Wide spread applicability Much easier through the use of computers Very clear results

Computer Optimization Simulated Annealing Genetic Algorithms Taboo Lists Limited to static scenarios

Ant Optimization Marco Dorigo in 1992 Simplistic agents Imprinting the environment Dynamic solution

Why Use NetLogo? Agent based environment Easy to use Graphical solution Appropriate output

Elements of my Model Patches - hold pheromone values Walls Food Source Hive or Ant Hill Ants – Carry food and read pheromone values

Ant Harvesting 101 Have food? Laying “pheromone highs” Pheromone gradients Find the strongest pheromone Walls and wrapping

Ant Harvesting 102 Found your destination? Pick up or deposit Switch modes

Put to the Test Double bridge experiments Originally performed by Deneubourg and colleagues (Deneubourg, Aron, Gross, and Pasteel) on real ants Testing ant optimization and foraging habits

Test 1 – Equal Length

Test 2 – Unequal Length

Test 3 – Appearing Bridges

Pheromone Evaporation Too slow and you get stuck on food sources Too fast and you can’t form trails Must be an optimal level

Testing Conditions Created a static environment Tested evaporation rates from 0%- 1% Ants return all food to the nest

Initial Results

Refining My Test

Conclusions Slow Evaporation Form trails faster and farther Pocketing Fast Evaporation Eliminates pocketing Relies on higher ant density

The End