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  2. Ant colony optimization algorithms - Wikipedia

    en.wikipedia.org/wiki/Ant_colony_optimization...

    As an example, ant colony optimization [3] is a class of optimization algorithms modeled on the actions of an ant colony. [4] Artificial 'ants' (e.g. simulation agents) locate optimal solutions by moving through a parameter space representing all possible solutions.

  3. Humanoid ant algorithm - Wikipedia

    en.wikipedia.org/wiki/Humanoid_Ant_algorithm

    The humanoid ant algorithm (HUMANT) [1] is an ant colony optimization algorithm. The algorithm is based on a priori approach to multi-objective optimization (MOO), which means that it integrates decision-makers preferences into optimization process. [2] Using decision-makers preferences, it actually turns multi-objective problem into single ...

  4. Simulated annealing - Wikipedia

    en.wikipedia.org/wiki/Simulated_annealing

    Graduated optimization digressively "smooths" the target function while optimizing. Ant colony optimization (ACO) uses many ants (or agents) to traverse the solution space and find locally productive areas. The cross-entropy method (CE) generates candidate solutions via a parameterized probability distribution. The parameters are updated via ...

  5. Swarm behaviour - Wikipedia

    en.wikipedia.org/wiki/Swarm_behaviour

    Ant colony optimization is a widely used algorithm which was inspired by the behaviours of ants, and has been effective solving discrete optimization problems related to swarming. [31] The algorithm was initially proposed by Marco Dorigo in 1992, [ 32 ] [ 33 ] and has since been diversified to solve a wider class of numerical problems.

  6. Stigmergy - Wikipedia

    en.wikipedia.org/wiki/Stigmergy

    The network of trails functions as a shared external memory for the ant colony. [8] In computer science, this general method has been applied in a variety of techniques called ant colony optimization, which search for solutions to complex problems by depositing "virtual pheromones" along paths that appear promising. [9]

  7. Category:Optimization algorithms and methods - Wikipedia

    en.wikipedia.org/wiki/Category:Optimization...

    Bacterial colony optimization; Barzilai-Borwein method; Basin-hopping; Benson's algorithm; Berndt–Hall–Hall–Hausman algorithm; Bin covering problem; Bin packing problem; Bland's rule; Branch and bound; Branch and cut; Branch and price; Bregman Lagrangian; Bregman method; Broyden–Fletcher–Goldfarb–Shanno algorithm

  8. Swarm intelligence - Wikipedia

    en.wikipedia.org/wiki/Swarm_intelligence

    Ant colony optimization (ACO), introduced by Dorigo in his doctoral dissertation, is a class of optimization algorithms modeled on the actions of an ant colony. ACO is a probabilistic technique useful in problems that deal with finding better paths through graphs.

  9. List of metaphor-based metaheuristics - Wikipedia

    en.wikipedia.org/wiki/List_of_metaphor-based...

    The ant colony optimization algorithm is a probabilistic technique for solving computational problems that can be reduced to finding good paths through graphs.Initially proposed by Marco Dorigo in 1992 in his PhD thesis, [1] [2] the first algorithm aimed to search for an optimal path in a graph based on the behavior of ants seeking a path between their colony and a source of food.