[PDF] ant colony optimization algorithm steps

Pseudo Code:
  • Procedure ACO:
  • Initialize the necessary parameters and pheromone concentration;
  • while not termination do:
  • Generate initial ant population;
  • Calculate the fitness values for each ant of the colony;
  • Find optimal solution using selection methods;
  • Update pheromone concentration;
  • end while.
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  • How does an ant colony algorithm work?

    The ant colony algorithm is an algorithm for finding optimal paths that is based on the behavior of ants searching for food. At first, the ants wander randomly. When an ant finds a source of food, it walks back to the colony leaving "markers" (pheromones) that show the path has food.

  • What is the ant line optimization algorithm?

    The ACO algorithm models the behavior of real ant colonies, in which individual ants communicate with each other by depositing and following pheromone trails. The algorithm uses this behavior to iteratively construct solutions to an optimization problem, with the pheromone trails representing the solution components.
  • Ant Colonies Optimization (ACO) is adopted from the behavior of ants in finding pathways, as a multi agent simulation that uses natural ant metaphors to solve physical space problems. Ants can build a path from the nest to the food source and then return to the nest via the fastest path.
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