Ant colony algorithm - PowerPoint PPT Presentation

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Ant colony algorithm

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Ant colony algorithm Ant colony algorithm mimics the behavior of insect colonies completing their activities Ant colony looking for food Solving a problem – PowerPoint PPT presentation

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Title: Ant colony algorithm


1
Ant colony algorithm
  • Ant colony algorithm mimics the behavior of
    insect colonies completing their activities

Ant colony looking for food lt gt
Solving a problem N Individual ants
lt gt N Solutions Each time
the colony goes to lt gt Population
of N solutions look for food and returns to the
nest
2
Ant colony algorithm
  • Ant colony algorithm mimics the behavior of
    insect colonies completing their activities
  • Collective process
  • Some kind of invisible agent (having a
    global memory of the process) is coordinating and
    having an impact on the behavior of each
    individual
  • The solutions of the current population
    (associated with the individual insects) are used
    to update this global memory (trace of pheromone)

Ant colony looking for food lt gt
Solving a problem N Individual ants
lt gt N Solutions Each time
the colony goes to lt gt Population
of N solutions look for food and returns to the
nest
3
Ant colony algorithm
  • Ant colony algorithm mimics the behavior of
    insect colonies completing their activities
  • Individual process
  • A new feasible solution (corresponding to
    an ant going out to look for food) is generated
    by means of a constructive method (the ant moving
    forward) that uses the information in the global
    memory of the invisible agent

Ant colony looking for food lt gt
Solving a problem N Individual ants
lt gt N Solutions Each time
the colony goes to lt gt Population
of N solutions look for food and returns to the
nest
4
Ant colony algorithm
  • Ant colony algorithm well suited for
    assignment-type problem

5
Ant colony algorithm
  • Ant colony algorithm well suited for
    assignment-type problem
  • Contructing a new solution (corresponding to an
    ant)

6
Ant colony algorithm
  • Ant colony algorithm well suited for
    assignment-type problem
  • Contructing a new solution (corresponding to an
    ant)
  • - In traditional construction procedure
    (Greedy, GRASP, for instance), at each iteration
    we select an activity and a resource to assigned
    to it, according to the best desirability of the
    pair ( for instance, to optimize the
    objective function given the values of the
    variables already fixed)

7
Ant colony algorithm
  • Ant colony algorithm well suited for
    assignment-type problem
  • Contructing a new solution (corresponding to an
    ant)
  • - In traditional construction procedure
    (Greedy, GRASP, for instance), at each iteration
    we select an activity and a resource to assigned
    to it according to the best desirability of the
    pair ( for instance, to optimize the
    objective function given the values of the
    variables already fixed)
  • - In ant colony algorithm, at each iteration
    the selection of the pair activity resource is
    made according to the desirability of the pair
    and also according to past history included in
    the global memory

8
Iteration of an ant colony algorithm
9
Graph coloring problemGreedy vs Ant Colony
  • Graph coloring problem
  • Vertices are ordered in
  • decreasing order of their degree
  • Vertices selected in that order
  • For each vertex, select a color in order to
    reduce the number of pairs of adjacent vertices
    already colored with the same color
  • Graph coloring problem
  • Vertices are ordered in
  • decreasing order of their degree
  • Vertices selected in that order
  • For each vertex, select a color in order to
    reduce the number of pairs of adjacent vertices
    already colored with the same color
  • and accounting for the quality of solutions
    where the vertex has the color.
  • Impact of a given solution decreases with
    the number of iterations since it was generated

10
Graph coloring Selecting vertex
11
Graph coloring Selecting color
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