Editing Genetic algorithm (section)

====Swarm intelligence====
{{main|Swarm intelligence}}
Swarm intelligence is a sub-field of [[Evolutionary Computation|evolutionary computing]].

* [[Ant colony optimization]] ('''ACO''') uses many ants (or agents) equipped with a pheromone model to traverse the solution space and find locally productive areas. 
*Although considered an [[Estimation of distribution algorithm]],<ref>{{cite journal|last1=Zlochin|first1=Mark|last2=Birattari|first2=Mauro|last3=Meuleau|first3=Nicolas|last4=Dorigo|first4=Marco|title=Model-Based Search for Combinatorial Optimization: A Critical Survey|journal=Annals of Operations Research|date=1 October 2004|volume=131|issue=1–4|pages=373–395|doi=10.1023/B:ANOR.0000039526.52305.af|language=en|issn=0254-5330|citeseerx=10.1.1.3.427|s2cid=63137}}</ref> [[Particle swarm optimization]] (PSO) is a computational method for multi-parameter optimization which also uses population-based approach. A population (swarm) of candidate solutions (particles) moves in the search space, and the movement of the particles is influenced both by their own best known position and swarm's global best known position. Like genetic algorithms, the PSO method depends on information sharing among population members. In some problems the PSO is often more computationally efficient than the GAs, especially in unconstrained problems with continuous variables.<ref>Rania Hassan, Babak Cohanim, Olivier de Weck, Gerhard Vente
r (2005) [https://www.mit.edu/~deweck/PDF_archive/3%20Refereed%20Conference/3_50_AIAA-2005-1897.pdf A comparison of particle swarm optimization and the genetic algorithm]</ref>