Algorithmic Complexity
In flocking simulations, there is no central control; each bird behaves autonomously. In other words, each bird has to decide for itself which flocks to consider as its environment. Usually environment is defined as a circle (2D) or sphere (3D) with a certain radius (representing reach).
A basic implementation of a flocking algorithm has complexity - each bird searches through all other birds to find those which fall into its environment.
Possible improvements:
- bin-lattice spatial subdivision. Entire area the flock can move in is divided into a large number of bins. Each bin stores which birds it contains. Each time a bird moves from one bin to another, lattice has to be updated.
- Example: 2D(3D) grid in a 2D(3D) flocking simulation.
- Complexity:, k is number of surrounding bins to consider; just when bird's bin is found in
Lee Spector, Jon Klein, Chris Perry and Mark Feinstein studied the emergence of collective behavior in evolutionary computation systems.
Read more about this topic: Flocking (behavior)
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