Answers.Body: <p>The first ant colony optimisation algorithm was introduced by <a href="http://iridia.ulb.ac.be/~mdorigo/HomePageDorigo/" rel="nofollow noreferrer">Marco Dorigo</a> in the report <em>Positive Feedback as a Search Strategy</em> (1991) and his PhD thesis <em>Optimization, Learning and Natural Algorithms</em> (1992). He's still one of the leading figures in the field of <a href="https://en.wikipedia.org/wiki/Swarm_intelligence" rel="nofollow noreferrer">swarm intelligence</a> (having also written or co-written several papers and books). Another important person that contributed to ACO algorithms is <a href="http://people.idsia.ch/~luca/" rel="nofollow noreferrer">Luca Gambardella</a> (co-director of <a href="http://www.idsia.ch/" rel="nofollow noreferrer">IDSIA</a>).</p>
  
  <p>There are several ACO algorithms. They are all based on the way real ants behave, that is, by leaving a substance called "pheromone" on the ground in order to communicate. More specifically, the amount of pheromone is associated with value (e.g. food): more pheromone means more value. (It should now be clear the reason behind the queues real ants form).</p>
  
  <p>A list of ACO algorithms can be found at <a href="http://iridia.ulb.ac.be/~mdorigo/ACO/publications.html#THIRD" rel="nofollow noreferrer">http://iridia.ulb.ac.be/~mdorigo/ACO/publications.html</a>. For reproducibility, here's a (non-exhaustive) list:</p>
  
  <ul>
  <li>Ant System</li>
  <li>Elitist Ant System</li>
  <li>Ant-Q</li>
  <li>Ant Colony System</li>
  <li>Max-Min Ant System</li>
  <li>Rank-based Ant System</li>
  <li>ANTS</li>
  <li>Hyper Cube - ACO</li>
  </ul>
  
  <p>ACO algorithms have been applied to <strong><a href="https://en.wikipedia.org/wiki/Combinatorial_optimization" rel="nofollow noreferrer">combinatorial</a></strong> and <strong><a href="https://en.wikipedia.org/wiki/NP-completeness" rel="nofollow noreferrer">NP-complete</a></strong> (e.g. the <a href="https://en.wikipedia.org/wiki/Travelling_salesman_problem" rel="nofollow noreferrer">travelling salesman problem</a>) problems. ACO algorithms are thus a collection of meta-heuristic and probabilistic algorithms (in the same family of <a href="https://en.wikipedia.org/wiki/Simulated_annealing" rel="nofollow noreferrer">simulated annealing</a>) to tackle often considered <a href="https://en.wikipedia.org/wiki/Computational_complexity_theory#Intractability" rel="nofollow noreferrer">intractable problems</a>. The <a href="https://en.wikipedia.org/wiki/Ant_colony_optimization_algorithms#Applications" rel="nofollow noreferrer">related Wikipedia article</a> contains a more exhaustive section dedicated to the applications of these algorithms. ACO algorithms are often combined with <a href="https://en.wikipedia.org/wiki/Local_search_(optimization)" rel="nofollow noreferrer">local search algorithms</a> (like the 2-opt or 3-opt).</p>
  
  <p>I would suggest you to start with the travelling salesman problem, which was the first application of these algorithms. You can have a look at the reference implementations at <a href="http://iridia.ulb.ac.be/~mdorigo/ACO/aco-code/public-software.html" rel="nofollow noreferrer">http://iridia.ulb.ac.be/~mdorigo/ACO/aco-code/public-software.html</a>, where you can also find software to solve specific tasks (not just the TSP, such as maximum clique problems).</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-05-27T20:04:05.640Z
Answers.Text: that's very useful, thanks, but I'm looking for a new topic to apply this algorithm, any ideas ?
Answers.UserDisplayName: Hamza Dabjan
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Answers.Text: @HamzaDabjan Maybe you could look at some NP-complete problem that people have not yet tackled using ACO. See e.g. this list [https://en.wikipedia.org/wiki/NP-completeness](https://en.wikipedia.org/wiki/NP-completeness#NP-complete_problems).
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Body: <p>I'm interested in ant colony optimization algorithms and bee algorithms,but I'm confused what are the applications of these algorithms</p>
  
  <p>Can you suggest me some examples of applications can I work on?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-05-26T22:12:02.277Z
Comments.Text: Ant colony algorithms are used for path searching and routing if I'm not mistaken
Comments.UserDisplayName: Miguel Saraiva
Tags: <applications><swarm-intelligence><ant-colony-optimization>
Title: What are the applications of ant colony optimization algorithms?
UserDisplayName: Hamza Dabjan
ThreadText: # What are the applications of ant colony optimization algorithms?
  
  (Asked by: username_0 on 2019-05-26)
  
  I'm interested in ant colony optimization algorithms and bee algorithms,but I'm confused what are the applications of these algorithms
  
  Can you suggest me some examples of applications can I work on?
  
  **Tags:** applications, swarm-intelligence, ant-colony-optimization
  > **Comments:**
  > **username_1**: Ant colony algorithms are used for path searching and routing if I'm not mistaken  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2019-05-26. Score: 2 | Accepted answer
  
  The first ant colony optimisation algorithm was introduced by [Marco Dorigo](http://iridia.ulb.ac.be/~mdorigo/HomePageDorigo/) in the report *Positive Feedback as a Search Strategy* (1991) and his PhD thesis *Optimization, Learning and Natural Algorithms* (1992). He's still one of the leading figures in the field of [swarm intelligence](https://en.wikipedia.org/wiki/Swarm_intelligence) (having also written or co-written several papers and books). Another important person that contributed to ACO algorithms is [Luca Gambardella](http://people.idsia.ch/~luca/) (co-director of [IDSIA](http://www.idsia.ch/)).
  
  There are several ACO algorithms. They are all based on the way real ants behave, that is, by leaving a substance called "pheromone" on the ground in order to communicate. More specifically, the amount of pheromone is associated with value (e.g. food): more pheromone means more value. (It should now be clear the reason behind the queues real ants form).
  
  A list of ACO algorithms can be found at [http://iridia.ulb.ac.be/~mdorigo/ACO/publications.html](http://iridia.ulb.ac.be/~mdorigo/ACO/publications.html#THIRD). For reproducibility, here's a (non-exhaustive) list:
  
  * Ant System
  * Elitist Ant System
  * Ant-Q
  * Ant Colony System
  * Max-Min Ant System
  * Rank-based Ant System
  * ANTS
  * Hyper Cube - ACO
  
  ACO algorithms have been applied to **[combinatorial](https://en.wikipedia.org/wiki/Combinatorial_optimization)** and **[NP-complete](https://en.wikipedia.org/wiki/NP-completeness)** (e.g. the [travelling salesman problem](https://en.wikipedia.org/wiki/Travelling_salesman_problem)) problems. ACO algorithms are thus a collection of meta-heuristic and probabilistic algorithms (in the same family of [simulated annealing](https://en.wikipedia.org/wiki/Simulated_annealing)) to tackle often considered [intractable problems](https://en.wikipedia.org/wiki/Computational_complexity_theory#Intractability). The [related Wikipedia article](https://en.wikipedia.org/wiki/Ant_colony_optimization_algorithms#Applications) contains a more exhaustive section dedicated to the applications of these algorithms. ACO algorithms are often combined with [local search algorithms](https://en.wikipedia.org/wiki/Local_search_(optimization)) (like the 2-opt or 3-opt).
  
  I would suggest you to start with the travelling salesman problem, which was the first application of these algorithms. You can have a look at the reference implementations at <http://iridia.ulb.ac.be/~mdorigo/ACO/aco-code/public-software.html>, where you can also find software to solve specific tasks (not just the TSP, such as maximum clique problems).
  
  > **Comments:**
  > **username_0**: that's very useful, thanks, but I'm looking for a new topic to apply this algorithm, any ideas ?  
  > **username_2**: @HamzaDabjan Maybe you could look at some NP-complete problem that people have not yet tackled using ACO. See e.g. this list [https://en.wikipedia.org/wiki/NP-completeness](https://en.wikipedia.org/wiki/NP-completeness#NP-complete_problems).  
  
  ---
  
  