1. by Eric Han, Chung Min Kim, and Kathryn Tarver Investigations of Ant Colony Optimization

2. An Introduction to Ants 10,000+ species of ants around the world Eat seeds, nectar, fungi, insects, etc. Colonies led by queens

3. How Ants Forage for Food 1.Random walk 2.Pheromone is dropped 3.Food source quality affects pheromone amount 4.More pheromone = favored path 5.Pheromone evaporates

4. ACO in Action

8. ACO: Ant Colony Optimization ●First suggested by Marco Dorigo (1992) ●Inspired by foraging ant colonies ●Algorithm sends particles on random walks to optimize pathways ●Currently applied to problems such as Internet routing and protein folding

9. Our goal is to: 1.create an algorithm to find the shortest path between two points in a network, and 2.explore the effects of changing parameters in the algorithm. Project Goal

10. Pseudocode for each iteration: 1.run ants 2.add pheromone 3.evaporate pheromone

11. Objects 0 1 2 3 4 2 2 1 5 3 1

12. Parameters ParameterDescription pRate of pheromone evaporation q Scalar, proportional to amount of pheromone added to path colsizeNumber of ants

13. Pseudocode: The Ants run ants for each ant: while not at end vertex: mark current vertex as visited for all unvisited vertices: roll RNG to see if traveling this vertex if traveling: move to the vertex add vertex to path

14. Pseudocode: The Edges add pheromone: for each ant for each edge along its path add (Q÷L) pheromone to the edge evaporate pheromone: for each edge multiply pheromone value by (1-p)

15. Simulation

16. Observation 1 More ants → Less Noise, fewer convergences onto local optimal

17. Observation 2 Weight scaling → decreases # ants taking optimal path

18. Observation 3 Increasing pheromone evaporation rate → increases % of ants taking optimal path

19. Observation 4 Increasing amount of pheromone added → no effect

20. General Observations ●Large, dense graph o sometimes will find global optimal o usually will converge on local optimal  comes close to the global optimal o need many ants to avoid local optimal ●Small, dense graph o ants almost always find global optimal o don’t need as many ants or iterations to do so o converges more slowly

21. Conclusions ●To maximize ants taking best path: o high evaporation rate o large colony o smaller path weights

22. Discussion: What Now? ●Dynamic graphs ●Eliminate convergences onto local optimal ●Optimize running time ●Analytically determine effects of changing parameters

23. Bibliography Ants, Ant Pictures, Ant Facts - National Geographic. (n.d.). Retrieved July 20, 2015. Argentine Ant l Globe spanning insect society - Our Breathing Planet. (n.d.). Retrieved July 20, 2015. Blum, C., & Li, X. (2008). Swarm Intelligence in Optimization. Natural Computing Series Swarm Intelligence, 43-85. Retrieved July 20, 2015. Priyadi, A. Ant fire [Online image]. Retrieved July 20, 2015 from http://yourshot.nationalgeographic.com/photos/3098725/?source=gallery. Ant clipart [Online image]. (2014). Retrieved July 30, 2015 from ……… http://www.clipartpanda.com/clipart_images/ant-clipart-158500 http://www.pageresource.com/clipart/clipart/animals/insects/ants/ant-3.png Thank you!