1. Presented by: Martyna Kowalczyk CSCI 658
Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem
Paper by: Marco Dorigo and Luca Maria Gambardella
Presented by: Martyna Kowalczyk
CSCI 658

2. Basic Idea nature-inspired
real ants are capable of finding the shortest path from a food source to their nest
no use of visual cues; exploit pheromone information

3. Basic Idea

4. Ant System first ACO procedure published in 1992 by Marco Dorigo
has been improved since then

5. Ant System Process
each ant generates complete tour by choosing cities according to a probabilistic state transition rule
when all ants are done, global pheromone updating rule is applied
long-term memory = pheromone

6. Ant Colony System improved efficiency when applied to TSP
3 major changes to AS:
new state transition rule
global updating rule applied only to edges belonging to the best ant tour
local pheromone updating rule

7. ACS Algorithm

8. ACS State Transition Rule

9. AS State Transition Rule
(S in the ACS state transition rule)

10. ACS Local Updating Rule

11. ACS Global Updating Rule

12. ACS Parameter Settings
All experiments had parameters set to:
β = 2
α = ρ = 0.1
q0 = 0.9
τ0 = (n Lnn)-1
number of ants = m = 10
ants are initially placed randomly with at most 1 ant in each city

13. Cooperation Among Ants
ACS effectively exploits pheromone-mediated cooperation
cooperating vs. non cooperating ants

14. Cooperation Experiment 1

15. Cooperation Experiment 2

16. Comparison with Other Heuristics
considered two sets of TSP problems:
five randomly generated 50-city problems
three geometric problems between 50 and 100 cities

17. Comparison with Other Heuristics

18. Comparison with Other Heuristics

19. Improvements To Be Made
number of ants that should contribute to global updating rule
move from current parallel local updating of pheromone to a sequential one
add more effective local optimizer

20. Thank you!