In this post I’ll explain and demonstrate an algorithm that simulates a group of entities grouping together, illustrating something called “flocking”. I think it’s quite neat because the flock exhibits some complex collective intelligence when just a few simple governing rules are applied to each entity. The original flocking algorithm was developed by Craig Reynolds in 1986, and has some super cool real world applications:
- Computer animation. Batman Returns (1992) is widely quoted as having been nominated for an Oscar for its bat swarms which were procedurally generated using algorithms similar to these.
- Social network simulation and modeling opinion flow. After choosing humans as the entities in the flock, the overall direction of the flock can be estimated using the rules that apply to the simple flock model, and people’s future opinions can be predicted. See Gamasutra’s stupendous article on the subject.
- Aerospace engineering. By sending UAVs on missions in flocks they are able to more effectively complete their missions and react to enemy events. See one paper and another on the subject.
- Distributed systems analysis, search, and optimization. By modeling things like spacial data, network traffic, or solutions to an optimization problem as entities, the direction of the flock can be used to find clusters, where to push traffic, or optimal solutions.
for more info : http://harry.me/2011/02/17/neat-algorithms---flocking/
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