ANALYSIS OF BOID ALGORITHM AS A METHOD FOR ANIMAL FLOCKING SIMULATION USING ALIGNMENT CLUSTERING INDEX
Collective animal behavior exhibits intersting phenomenon found in nature. As they However these phenomenon can be complicated and challenging to simulate and visualize. In 1987 Craig Reynolds introduced an algorithm called boid or bird-oid which simulates bird-like flocking behavior. Comprising...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/77479 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Collective animal behavior exhibits intersting phenomenon found in nature. As they
However these phenomenon can be complicated and challenging to simulate and
visualize. In 1987 Craig Reynolds introduced an algorithm called boid or bird-oid
which simulates bird-like flocking behavior. Comprising specific rules, the
algorithm captures individual motion within a flock, later categorized into three
components by its creator in 1999 that is: cohesion, alignment, and separation.
These rules encourage agents in the simulation to interact and move collectively.
This research aims to analyze boid using its simulation. In order to modify how
each rule influence the boid movement, a weight value is assigned. These values
are varied to obtain the data needed for analysis. Futhermore, additional rules such
as bound, containment, and wander are incorporated to the simulation. The
simulations are executed using the C#-based Unity engine, version 2020.3.3f1 LTS.
The analysis is performed using the alignment clustering index (ACI) due to
periodic flocking movement within the same area. Results reveal that alignment is
the most influential factor in collective behavior, as evidenced by the increasing
average ACI values corresponding to higher alignment weights. |
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