Particle swarm optimization (PSO) module for Haskell
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A library for PSO

This is a small library for doing Particle Swarm Optimization (PSO) in Haskell. For an overview of what PSO is, see the paper "Particle Swarm Optimization" by James Kennedy and Russel Eberhart. Alternately, wikipedia's entry is pretty good.

If you want to know the specific flavor of algorithm that this library is based on, consult

Bratton, Daniel, and James Kennedy. "Defining a standard for particle swarm optimization." Swarm Intelligence Symposium, 2007. SIS 2007. IEEE. IEEE, 2007.

Note that while updating based on the so-called "local swarm" is desired, it is not yet implemented. This library uses the slightly-less effective "global swarm". Otherwise, this library conforms perfectly to the standard PSO given in the paper above (although other PSO models are supported as well).


Let's start with an example, and then enumerate all of the ways in which your needs might differ from the example. We want to find the minimum value of the function

f :: (Double, Double) -> Double
f (x,y) = x^2 + y^2

First, we'll load the module

import Pso

If we have no strong feelings, we can just use easyOptimize

easyOptimize :: (PsoVect a, Random a, Grade b) 
    => (a -> b)  -- Function to optimize
    -> (a, a)    -- Bounds to create particles within
    -> Integer   -- Number of iterations
    -> StdGen    -- Generator to use
    -> PsoGuide a b

So if we wish to look at points (x, y) with -5 <= x <= 5 and -5 <= y <= 5, we can call

main = do
    gen <- newStdGen
    let guide = easyOptimize f ((-5, -5), (5, 5)) 1000 gen
    putStrLn $ "The minimum appears to be: " ++ (show $ val guide)
    putStrLn $ "It occurs at: " ++ (show $ pt guide)

We can see that the object returned (PsoGuide (Double, Double) Double) contains both the minimum value of f and the point at which it occurs.

The function easyOptimize is fun, but doesn't give us a lot of control. In general, we might want to see all attempts at a solution, not just the 1000th. Then we can better decide what the minimum might be.

To do this, we'll need to create a Swarm (Double, Double) Double. We can use the function defaultSwarm.

defaultSwarm :: (PsoVect a, Random a, Grade b)
    => (a -> b)  -- ^ Function to optimize
    -> (a, a)    -- ^ Bounds to begin search
    -> StdGen    -- ^ Random generator
    -> (Swarm a b, StdGen)

Once we've created a swarm, we'll want to update it. In fact, it would be best if we could just update it repeatedly, obtaining an infinite list of better and better swarms.

iterateSwarm :: (PsoVect a, Grade b) => Swarm a b -> StdGen -> [Swarm a b]

Because defaultSwarm returns a Swarm and a StdGen, we can call both of these in order with

let ss = (uncurry iterateSwarm) $ defaultSwarm f ((-5, -5), (5, 5)) gen

This gives us a list of swarms, each one (hopefully) better than the last. There are a number of things that we can do with this - we can look to see when we stop seeing improvements for, say, 20 steps.

putStrLn . show . head . head . dropWhile ((<=20) . length) . group . map (val . gGuide) $ ss

Or, we could look to see when all of our particles have started to cluster. There is a built-in function for this.

posVariance :: (PsoSized a) => Swarm a b -> Double

posVariance measures the variance of the distances of the particles (actually, the squares of the distances). We could find the value once this variance drops below 0.000001.

putStrLn . show . gGuide . head . dropWhile ((> 0.000001) . posVariance) $ ss

Our modified main looks like

main = do
    gen <- newStdGen
    let ss = (uncurry iterateSwarm) $ defaultSwarm f ((-5, -5), (5, 5)) gen
    putStrLn "No improvements for 20 steps:"
    putStrLn . show . head . head . dropWhile ((<=20) . length) . group . map (val . gGuide) $ ss
    putStrLn "Variance of particle positions below 0.000001:"
    putStrLn . show . gGuide . head . dropWhile ((> 0.000001) . posVariance) $ ss

Looks like there is a minimum of 0 near (0,0). At least, at first glance.

My function is different!

We don't always want to minimize a function f :: (Double, Double) -> Double, and we don't always want to perform the default search. Here are some ways that this library can handle optimization of other functions.

Different input

Maybe you would rather minimize a function

f :: (Double, Double, Double) -> Double

Or, more generally, just some function

f :: a -> Double

Short story: you need a to be an instance of PsoVect. Built in instances (in Calypso.Instance.Grade and Calypso.Instance.PsoVect) include:

(Double, Double)
(Double, Double, Double)
(Double, Double, Double, Double)
(Double, Double, Double, Double, Double)

Replace any of the above Double values with Float or Rational, and you are still just fine. In, fact, you can replace any of the above Double with any other instance of PsoVect and you are still fine. Ex:

(Double, (Float, Double), (Float, Float, Rational))
(Double, (Double, (Double, Double)))

But I only go up to five. Why five? Because if you have more than that, you probably want a PsoList. It's a wrapper for a list that knows how long it is.

fromList :: [a] -> PsoList a
toList :: PsoList a -> [a]

It works so long as a is an instance of PsoVect. Why do I need to wrap my lists? Because of reasons (if you are interested, check the code or email me).

If none of these are what you want, then you have two options:

  1. Construct isomorphism functions to one of these data types.
  2. Make your data type an instance of PsoVect.

The former is much easier, but the latter may be cleaner. I don't have an example of these yet, but I'm working on one.

Different output

Maybe you want to minimize a function

f :: (Double, Double) -> Int

or, more generally,

f :: (Double, Double) -> b

That's fine. You need b to be an instance of the type Grade. In Calypso.Instance.Grade, I include instances for


All are built so that < means "better than". If you want to make your own type an instance of Grade, all you need to do is define either betterThan or worseThan.

class Grade b
    betterThan :: b -> b -> Bool
    worseThan  :: b -> b -> Bool

There is also an instance for Maybe b, where b is one of the grades above. See the section Bounded Searching for more details.

I'd rather maximize

Maybe you want to maximize a function f :: (Double, Double) -> Double. In that case, it's probably easiest to just minimize negate . f.

However, if you'd rather not confuse yourself with extra negates all over, all you need to do is create a new instance of Grade Double with

instance Grade Double
    betterThan = (>)

You'll need to omit the module where the default instance is loaded, so your import statements will look more like

import Calypso.Core
-- import Calypso.Instances.Grade
import Calypso.Instances.PsoVect

Bounded searching

Sometimes, we don't want to find the global minimum, just the minimum subject to certain restrictions. For instance, suppose we want to minimize

f :: (Double, Double) -> Double
f (x, y) = 4 * x + 2 * y - 3

subject to the constraint 1 <= x <= 3 and 0 <= y <= 2. One way to think about this is to modify our function:

f' :: (Double, Double) -> Maybe Double
f' (x, y)
    | withinBounds = Just $ 4 * x + 2 * y - 3
    | otherwise    = Nothing
    withinBounds = 1 <= x &&
                   x <= 3 &&
                   0 <= y &&
                   y <= 2

We can read this as: outside of the bounds, our function is so bad that you shouldn't even consider it. Just don't. Our particles are allowed to leave the bounds, the function is just so bad out there that they will eventually be drawn back in.

Side note: in the paper "Defining a standard for particle swarm optimization" by Daniel Bratton and James Kennedy (2007), they discover that this method of letting the particles "fly free", rather than constraining them, performs very well in a variety of situations.

More swarm control

Suppose that you want to have a bit more control of your swarm. You'd like to choose a non-default update method, or a number of particles other than 50 (the default number).

You'll want to call either randomSwarm or createSwarm.

randomSwarm :: (PsoVect a, Random a, Grade b) 
    => StdGen   -- ^ A random seed
    -> Int      -- ^ Number of particles
    -> (a,a)    -- ^ Bounds to create particles in
    -> (a -> b)             -- ^ Function to optimize
    -> Updater a b          -- ^ Updater
    -> (Swarm a b, StdGen)  -- ^ (Swarm returned, new seed)
createSwarm :: (PsoVect a, Grade b)
    => [a]          -- ^ Positions of of particles
    -> (a -> b)     -- ^ Function to optimize
    -> Updater a b  -- ^ Updater to use
    -> Swarm a b

Read more about Updaters in the next section. The only difference is whether you have to supply the positions yourself or whether they will be randomly generated. In general, you want this, so you'll probably be using randomSwarm.

Alternate parameters

In general, each step we update each particle's velocity, and then use that velocity to update it's position. But there are a lot of ways to update the particle's velocity.

All of them involve two things:

  1. Find a pseudo-random vector guiding the particle towards its "private guide" (the best location it has found so far). Call it vp.
  2. Find a pseudo-random vector guiding the particle towards its "local guide" (the best location it's local swarm has found). Call it vl

Now, take these two things, multiply them by some parameters, and add them to the existing velocity (perhaps also multiplied by a parameter). Multiply the whole thing by a parameter (maybe), and then, if you feel like it, artificially reduce this to a maximum velocity. Also, some of these parameters might by dynamic, and adjust over time.

That's a lot of options.

All of this is handled via the Updater data type. Here is it's constructor:

data Updater a b = Updater {
    newVel :: StdGen -> Particle a b -> PsoGuide a b -> Integer -> (a, StdGen)

So an Updater is just a wrapper for a function that creates a new velocity for a particle using

  1. A StdGen, to do any necessary random calculations
  2. A Particle a b, from which it can get position, velocity, and th private guide
  3. A PsoGuide a b, the local guide for the particle
  4. An Int, the current iteration of the swarm. Useful for parameters that adjust over time

But really, you probably don't want to create one in this way. It's just useful to see what it is, and take the mystery out of it. Normally, you'll use one of

upDefault :: (PsoVect a, Grade b) => Updater a b
upStandard :: (PsoVect a, Grade b)
    => Double  -- ^ Constriction parameter (chi)
    -> Double  -- ^ Tendancy toward private guide (c1)
    -> Double  -- ^ Tendancy toward local guide (c2)
    -> Updater a b
upOriginal ::(PsoVect a, Grade b)
    => Double  -- ^ Tendancy toward private guide (c1)
    -> Double  -- ^ Tendancy toward local guide (c2)
    -> Updater a b
upInertiaWeight :: (PsoVect a, Grade b)
    => Double  -- ^ Inertia weight (omega)
    -> Double  -- ^ Tendancy toward private guide (c1)
    -> Double  -- ^ Tendancy toward local guide (c2)
    -> Updater a b
upInertiaWeightDynamic :: (PsoVect a, Grade b)
    => (Integer -> Double)  -- ^ Inertia weight (omega)
    -> Double               -- ^ Tendancy toward private guide (c1)
    -> Double               -- ^ Tendancy toward local guide (c2)
    -> Updater a b

These are all of the Updating methods that I found in the literature, except for those that reduce to a maximum velocity (which may be used with any of the above).

upVMax :: (PsoSized a, Grade b) => Double -> Updater a b

If you want to add a maximum velocity to one of these other updaters, you just combine them: Updater a b is an instance of Monoid! So

upVMax 5000 <> upOriginal 2 2

is an updater using the methods of the original paper (from 1995), plus a maximum velocity of 5000. You can actually use this Monoid structure to create loads of interesting Updaters very easily:

upStandard chi c1 c2 = (upScale chi) <> (upAddLocal c2) <> (upAddPrivate c1)

This is actually the definition of upStandard - it's just built from three little Updaters. For more info, consult the documentation.

Stack Overflow!

If you are using big swarms over a large-dimensional vector space for many iteration, and iterateSwarm eventually gives you a stack overflow, it may be because you are keeping track of every past version of your swarm all at once.

If you aren't interested in all this data (and let's be honest, unless you are examining the process of PSO, you aren't - you only care about the result), you can use iterateWhile

iterateWhile :: (PsoVect a, Grade b) 
    => (Swarm a b -> Bool)  -- ^ Condition to meet
    -> Swarm a b            -- ^ Swarm to update
    -> StdGen               -- ^ Random seed
    -> Swarm a b

It will update your swarm for as long as the condition specfied is met, ignoring the past and proceeding blindly ahead. My tests indicate that this doesn't build up the stack at all, but I do not yet understand haskell profiling, so I make no guarantees. You want examples?

Iterate until variance of particles is below 0.001:

iterateWhile ((> 0.001) . posVariance) s gen

Iterate until the grade of at least good is reached:

iterateWhile ((`worseThan` good) . val . gGuide) s gen

Iterate n times:

iterateWhile ((<n) . iteration) s gen

More Questions?

Consult the documentation! It can be built with

haddock -h -o docs Calypso.hs

Soon, I will have the library up on Hackage, and then you can browse the docs there.

Things to do

  • Improve documentation
  • Write example programs
  • Write test suite
  • Submit to Hackage