1. Guided By : Prof. Vineet Garg.
Presented By: Sulabh Pal,Suresh Kumar, Himanshu Srivastava.
Date: April 27th 2012.

3. • Section I (Introduction)
• Historical Background
Sulabh
• Ant System
Pal
• Section II
• The proposed scheme Himanshu
• Experimental results Srivastava
• Section III (Application+Conclusions)
• Application
• Conclusions and
• References Suresh
Kumar

5. Introduction

7. • Discrete optimization problems difficult to solve
• “Soft computing techniques” developed in past ten
years:
o Genetic algorithms (GAs)
 based on natural selection and genetics
o Ant Colony Optimization (ACO)
 modeling ant colony behavior

8. • Optimization Technique Proposed by Marco
Dorigo in the early ’90
• Often applied to TSP (Travelling Salesman
Problem): shortest path between n nodes

11. • A stochastic construction procedure
• Probabilistically build a solution
• Iteratively adding solution components to partial
solutions
- Heuristic information
- Pheromone trail
• Reinforcement Learning reminiscence
• Modify the problem representation at each
iteration

12. • Ants work concurrently and independently
• Collective interaction via indirect communication
leads to good solutions

13. Basic Phenomenon of Ant For
Finding Food

15. Random Way To Find The Way

16. • The pheromone
concentration on trail B will
increase at a higher rate
than on A, and soon the
ants on route A will choose
• Since the route B is to follow route B
shorter, the ants on this • Since most ants will no
path will complete the longer travel on route A, and
travel more times and since the pheromone is
thereby lay more volatile, trail A will start
pheromone over it. evaporating
• Only the shortest route will
remain!

17. • (a) The initial graph with distances.
• (b) At time t = 0 there is no trail on the graph edges.
• (c) At time t = 1 trail is stronger on shorter edges.

18. • Ants (blind) navigate from nest to food source
• Shortest path is discovered via pheromone trails
o each ant moves at random
o pheromone is deposited on path
o ants detect lead ant’s path, inclined to follow
o more pheromone on path increases probability of
path being followed

19. • Starting node selected at random
• Path selected at random
o based on amount of “trail” present on possible paths
from starting node
o higher probability for paths with more “trail”
• Ant reaches next node, selects next path
• Continues until reaches starting node
• Finished “tour” is a solution

20. • A completed tour is analyzed for optimality
• “Trail” amount adjusted to favor better solutions
o better solutions receive more trail
o worse solutions receive less trail
o higher probability of ant selecting path that is part of a
better-performing tour
• New cycle is performed
• Repeated until most ants select the same tour
on every cycle (convergence to solution)

21. • Algorithm in Pseudocode:
o Initialize Trail
o Do While (Stopping Criteria Not Satisfied) – Cycle Loop
 Do Until (Each Ant Completes a Tour) – Tour Loop
 Local Trail Update
 End Do
 Analyze Tours
 Global Trail Update
o End Do

23. NATURE
COMPUTER SCIENCE
NATURAL HABITAT GRAPH(NODES AND EDGES)
NEST AND FOOD NODES IN GRAPH;START AND
DESTINATION
ANTS AGENTS; OUR ARTIFICIAL
AGENTS
VISIBILITY THE RECIPROCAL OF
DISTANCE
PHEROMONES ARTIFICIAL PHEROMONES
FORAGING BEHAVIOUR Random walk through graph
(guided by pheromones)

24.  Scheme:
 Construct ant solutions
 Define attractiveness τ, based on experience from previous
solutions
 Define specific visibility function, η, for a given problem (e.g.
distance)
 Ant walk
 Initialize ants and nodes (states)
 Choose next edge probabilistically according to the
attractiveness and visibility
 Each ant maintains a tabu list of infeasible transitions for
that iteration
 Update attractiveness of an edge according to the number of
ants that pass
 through

25.  Pheromone update
 Parameter is called evaporation rate
 Pheromones = long-term memory of an ant colony
 ρ small low evaporation slow adaptation
 ρ large high evaporation fast adaptation
 Note: rules are probabilistic, so mistakes can be
made!
 “new pheromone” or Δτ usually contains the base
 attractiveness constant Q and a factor that you want to
 optimize
 (e.g. ) Q/length of tour

26. Path Selection Process

27. The proposed scheme
Experimental results

28. Edge The
detection proposed
ACO
Original image Edge image The compensatory edge image
The improved result

29. (Initialization)
(a). Original image (b). Traditional edge (c). Placing ants on all endpoints
detection of (a).

30. 0≦q≦1 If q≦q0
q0=0.9
q=0.5 ≦q0
q=0.3 ≦q0 otherwise
1 2
(Walk)
Pixel=1 Pixel=1 Pixel=1 Pixel=1 Pixel=1
Pixel=100 Pixel=100 Pixel=1 Pixel=100 Pixel=100
Pixel=100 Pixel=100 Pixel=1 Pixel=100 Pixel=100
G=24,V=0.2 G=74,V=74
Pixel=100 Pixel=1 Pixel=100 Pixel=100 Pixel=100
Pixel=1 Pixel=100 Pixel=100 Pixel=100 Pixel=100

31. 0≦q≦1 If q≦q0
q0=0.9
q=0.95≧q0
q=0.55≦q0 otherwise
(Walk)
Pixel=1 Pixel=1 Pixel=1 Pixel=1 Pixel=1
0.02 0.000001
Pixel=100 Pixel=100 Pixel=1 Pixel=100 Pixel=100
0.03
Pixel=100 Pixel=100 Pixel=1 Pixel=100 Pixel=100
High prob
Pixel=100 Pixel=1 Pixel=100 Pixel=100 Pixel=100
Pixel=1 Pixel=100 Pixel=100 Pixel=100 Pixel=100

32. (Stop)
(a). Two ants meet face to face (b). The ant touches the other’s track. (c). Blind alley
(d). Measurement of track density.

33. a. Original image c. 200 iterations e. =0.9, =0.8 g. 200 iterations
=0.9, =0.8
b. Sobel edge detector d. Combine b, c f. Combine b, e d. Combine b, g

34. a. Original image b. Canny edge detector d. The improved result from
(b) with 160 iterations.

35. a. Original image b. Canny edge detector
c. Traditional ACO d. The proposed approach

37. ROUTING IN COMM. NETWORKS
Routing task is performed by Routers.
Routers use “Routing Tables” to direct the data.
If your destination is node 5
next node to 3
4
6
3 1
5 2
2

38. ROUTING IN COMM. NETWORKS
Problem statement
• Dynamic Routing
At any moment the pathway of a message must be as
small as possible. (Traffic conditions and the structure of
the network are constantly changing)
• Load balancing
Distribute the changing load over the system and
minimize lost calls.

39. Computation time comparison per 20 iterations.

40. Comparision Between ACO and Genetic Algorithm

41. Application

42. Application
• TSP
• QAP
• Scheduling
• VRP
• Telecommunication Network
• Graph Coloring
• Water Distribution Network
• Swarm Intelligence
• etc

43. Example Regarding Swarm Algorithm

44. URBAN SOLID WASTE
COLLECTION TRAFFIC FLOW OPTIMIZATION

45. • Positive Feedback accounts for rapid discovery
of good solutions
• Distributed computation avoids premature
convergence
• The greedy heuristic helps find acceptable
solution in the early solution in the early stages
of the search process.
• The collective interaction of a population of
agents.

46. • Slower convergence than other Heuristics
• Performed poorly for TSP problems larger than 75
cities.
• No centralized processor to guide the AS towards
good solutions

47.  Can solve certain NP-Hard problems in Polynomial
time
 Directed-Random Search
 Allows a balance between using previous knowledge
and exploring new
 solutions
 Positive feedback for good solutions/Negative
feedback for
 bad solutions
 Approximately convergent
 Optimal if not absolutely correct solutions
 In certain examples of ACO, no one “ant”
 is required to actually complete an accurate
 solution

48.  Problem specific
 Limited to problems that can be simulated by
graphs and optimized
 Coding difficulties for different problems
 Ineffective utilization of previously acquired
information,
 specifically the global solution
 Depending on the design of the algorithm, it
can
 converge towards a (less optimal) solution.

49.  We might like to add factors to minimize the
time it takes
 to reach an acceptable solution.
 Use the elements of previous solutions
 This allows for faster convergence
 As we construct more and more
solutions, there is more
 information available about the probable
“right” choices to make
 The decision making process might weigh
exploration vs.
 heuristic value

50.  Ant System: what we just went over
 Ant Colony System:
 Pseudo-random proportion rule:
 at each decision point for an ant, it has a
probability (1-q0) of using the
 same probability function as in the Ant
System or q0 of picking the best
 next node based on previous solutions

51.  Global Trail Update: only the
 best solution since the start of
 the computation will globally
 update its pheromones
 Local Trail Update: all ants
 consume/decrease
 pheromones along the path
 that they travel
 Elitist Ant System:
 Both the global solution and each ant update
their edges with
 pheromones on each iteration
 Applications

52. • The proposed method efficiently improves edge
images detected by traditional approaches like
Sobel or Canny.
• The computation time of the proposed method is
shorter than traditional ACO.

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55. Thank you for your attention!