Agenda

1
IE 607 Heuristic OptimizationAnt Colony
Optimization

2
Double Bridge Experiment
3
Behavior of Real Ants

• Real Ants Find the Shortest Path to Food
  Resource
• Pheromone Is Laid by Ants along the Trail
• Pheromone Evaporates over Time
• Pheromone Intensity Increases with Number of
  Ants Using Trail
• Good Paths Are Reinforced And Bad Paths
  Gradually Disappear

4
ACO

• Meta-Heuristic Optimization Method
• Inspired by Real Ants
• First published by Marco Dorigo (1992) as
  his dissertation
• Is currently greatly expanding in
  applications and interest, mainly centered in
  Europe
• Positive Negative Feedback
• Constructive Greedy Heuristic
• Population-based Method

5
Application

• TSP
• QAP
• VRP
• Telecommunication Network
• Scheduling
• Graph Coloring
• Water Distribution Network
• etc

6
Methodology
ACO

• ACO Algorithm
• Set all parameters and initialize the pheromone
  trails
• Loop
• Sub-Loop
• Construct solutions based on the state transition
  rule
• Apply the online pheromone update rule
• Continue until all ants have been generated
• Apply Local Search
• Evaluate all solutions and record the best
  solution so far
• Apply the offline pheromone update rule
• Continue until the stopping criterion is reached

7
Methodology
Overview of ACO Algorithm

• Each ant represents a complete solution
• Online updating is performed each time after an
  ant constructed a solution more chance to
  exploration
• Local search is applied after all ants construct
  solutions
• Offline updating is employed after the local
  search allow good ants to contribute

8
Methodology
Parameters of ACO Algorithm

• Pheromone trail of combination (i,j)
• Local heuristic of combination (i,j)
• Transition probability of combination (i,j)
• Relative importance of pheromone trail
• Relative importance of local heuristic
• Determines the relative importance of
  exploitation versus exploration
• Trail persistence

9
Methodology

• Ant System (AS) the earliest version of ACO
• State Transition Probability
• Pheromone Update Rule

10
Methodology
ASelite ASrank
11
Methodology
Ant-Q Ant Colony System (ACS) Local
Updating (Online Updating) Global Updating
(Offline Updating)
Exploitation
Exploration
12
Methodology
Max-Min Ant System (MMAS) ANTS
13
Website Books

• http//iridia.ulb.ac.be/mdorigo/ACO/ACO.html
  
• Bonabeau E., M. Dorigo T. Theraulaz
  (1999). From Natural to Artificial Swarm
  Intelligence. New York Oxford University
  Press.
• Corne D., M. Dorigo F. Glover, Editors
  (1999). New Ideas in Optimisation.
  McGraw-Hill .