page_title: Ant colony optimization algorithms
section_title: Antennas optimization and synthesis
hierarchical_section_title: Ant colony optimization algorithms / Applications / Antennas optimization and synthesis
caption_attribution_description: Русский: Непетлевые вибраторы формата 10×10, синтезиро- ванные с помощью муравьиного алгоритма English: Unloopback vibrators 10×10, synthesized by means of ACO algorithm.
context_page_description: In computer science and operations research, the ant colony optimization algorithm is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Artificial Ants stand for multi-agent methods inspired by the behavior of real ants.
  The pheromone-based communication of biological ants is often the predominant paradigm used. Combinations of Artificial Ants and local search algorithms have become a method of choice for numerous optimization tasks involving some sort of graph, e.g., vehicle routing and internet routing. The burgeoning activity in this field has led to conferences dedicated solely to Artificial Ants, and to numerous commercial applications by specialized companies such as AntOptima.
  As an example, Ant colony optimization is a class of optimization algorithms modeled on the actions of an ant colony. Artificial 'ants' locate optimal solutions by moving through a parameter space representing all possible solutions. Real ants lay down pheromones directing each other to resources while exploring their environment.
context_section_description: To optimize the form of antennas, ant colony algorithms can be used. As example can be considered antennas RFID-tags based on ant colony algorithms (ACO), loopback and unloopback vibrators 10×10
caption: Unloopback vibrators 10×10, synthesized by means of ACO algorithm[74]
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