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 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
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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
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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
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Ant colony algorithm

• Ant colony algorithm well suited for
  assignment-type problem

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Ant colony algorithm

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

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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)

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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

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Iteration of an ant colony algorithm
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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

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Graph coloring Selecting vertex
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Graph coloring Selecting color