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
Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem Paper by: Marco Dorigo and Luca Maria Gambardella Presented by: Martyna.
Dec 21, 2015
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Ant Colony System: A Cooperative Learning Approach to the
Traveling Salesman Problem
Paper by: Marco Dorigo and Luca Maria Gambardella
Presented by: Martyna KowalczykCSCI 658
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
Basic Idea
Ant System
● first ACO procedure published in 1992 by Marco Dorigo
● has been improved since then
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
Ant Colony System
● improved efficiency when applied to TSP
● 3 major changes to AS:o new state transition ruleo global updating rule applied only to edges
belonging to the best ant touro local pheromone updating rule
ACS Algorithm
ACS State Transition Rule
AS State Transition Rule
(S in the ACS state transition rule)
ACS Local Updating Rule
ACS Global Updating Rule
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
Cooperation Among Ants
● ACS effectively exploits pheromone-mediated cooperation
● cooperating vs. non cooperating ants
Cooperation Experiment 1
Cooperation Experiment 2
Comparison with Other Heuristics
● considered two sets of TSP problems:o five randomly generated 50-city problemso three geometric problems between 50 and
100 cities
Comparison with Other Heuristics
Comparison with Other Heuristics
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
Thank you!
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