Uses old Rust 2015
|0.2.3||May 3, 2016|
|0.2.2||Mar 31, 2016|
|0.1.2||Mar 25, 2016|
#770 in Algorithms
37 downloads per month
Karaboga's Artificial Bee Colony in Rust (and in parallel!)
Documentation, including sample code.
There's also a demo, which animates the algorithm for your viewing pleasure. It also uses a couple useful techniques for applying the algorithm to a complex, persistent state.
The Artificial Bee Colony is an optimization algorithm. It considers a set of solution candidates by sending conceptual "bees" to work on those solutions. There are three kinds of bee:
- One worker bee is dedicated to each solution. Each time a worker bee runs, it looks at a solution near the current one, keeping each improvement.
- Observer bees are like workers, but are not dedicated to a single solution. Instead, they look at all of the active solutions and choose one randomly to work on. Observers usually prefer to work on higher-fitness solutions.
- A worker or observer whose solution appears to be a local maximum becomes a scout. Scouting entails generating a new, random solution to break out of a rut.
The ABC algorithm can be -- and has been -- applied to a variety of applications. As with many such algorithms, the logic is fairly agnostic about the domain that it works in. In fact, the prerequisites for using the algorithm are:
- a data structure with a solution,
- a way of generating new, random solutions,
- a way of generating solutions "near" an existing solution, and
- a fitness function to score solutions.
A solution could be a game-playing AI, a blueprint for a building, or just a
point in space, and the
abc crate treats them all alike. Simply implement
trait for a type of your choice, construct a
Hive, and start running.
Speaking of running,
abc supports two run modes:
- running for a fixed number of rounds and returning the best solution,
- running continuously until stopped, or
- running continuously in the background and sending each improved solution over a Rust channel.
abc crate takes advantage of Rust's excellent concurrency support to
explore the same space. This means that heavy computation can be distributed
across multiple CPU cores, or I/O-bound evaluation can run without blocking.
The hive maintains a queue of bees, and the threads each take bees from the
queue and apply the bees' logic to the solutions. So, at a given moment, there
is a different bee working in each thread.