1. Ant Optimization in NetLogo By: Stephen Johnson

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

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

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

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

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

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

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

9. 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

10. Test 1 – Equal Length

11. Test 2 – Unequal Length

12. Test 3 – Appearing Bridges

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

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

15. Initial Results

16. Refining My Test

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

18. The End