Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem Paper by: Marco...
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Transcript of Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem Paper by: Marco...
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
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 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
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
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