
A disk scheduling algorithm based on ant colony optimization (2009) [pdf] - sajjadium
https://sajjadium.github.io/files/pdccs2009diskscheduling_paper.pdf
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cgh
For those interested in optimization based on ant colony behaviour, the book
"Ant Colony Optimization" by Dorigo and Stutzle is the text to get. It's quite
old now (2004) and unfortunately hasn't seen a second edition. It's a bit of a
forgotten classic, in my opinion. I don't work professionally in this area but
I found it to be highly approachable. The various ACO algorithms are appealing
not only for their utility but also for their intuitiveness. I mean, it's easy
to visualise ants cruising around, leaving pheromone trails, and so forth.

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sajjadium
Ant colony, like other evolutionary algorithmic such as genetic algorithms, is
very effective for solving optimization problems!

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_raoulcousins
Does this need a (2009)?

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dang
Good catch! Added.

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jampekka
These "animal based" optimization algorithms are a really weird scene, and
based on my brief encounters with it I'm quite sceptical of it. A lot of it is
based on "models" of animal behavior, but in reality this is more a marketing
gimmic and connections to animal behavior is mostly in the imagination of the
authors. And for some reason the animal (and other) metaphors seem to be a
substitute for rigorous study of the algorithms' properties.

There's a good elaboration of this here:
[https://web.archive.org/web/20131102075645/http://antor.ua.a...](https://web.archive.org/web/20131102075645/http://antor.ua.ac.be/system/files/mme.pdf)

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jamesblonde
Ant-colony optimization is a search algorithm that is inspired by observing
the behaviour individual ants as they search for food and the emergent
behaviour of the colony. Ants perform random search, unless they either (1)
find food - then they way as direct a way back to the nest as they remember,
laying down a trail of pheromones as they walk, or (2) they smell a pheromone
trail and then follow it towards the source of food. When the source of food
is exhausted, the pheromones evaporate, so they can forget what they have
learnt. The emergent behaviour is that they can collectively solve problems
such as finding the shortest path to food, or solving the NP-complete
travelling salesman problem efficiently.

Reference:
[http://staff.washington.edu/paymana/swarm/stutzle99-eaecs.pd...](http://staff.washington.edu/paymana/swarm/stutzle99-eaecs.pdf)

