1 / 18

Ant Optimization in NetLogo

By: Stephen Johnson. Ant Optimization in NetLogo. 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.

lam
Download Presentation

Ant Optimization in NetLogo

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. By: Stephen Johnson Ant Optimizationin NetLogo

  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

More Related