metaheuristics (empty) → 0.0.8

raw patch · 6 files changed

+1099/−0 lines, 6 filesdep +Streamdep +arrowsdep +basesetup-changed

Dependencies added: Stream, arrows, base, random

Files

• Control/Metaheuristics.hs +207/−0
• LICENSE +674/−0
• MetaheuristicsDemo/SimpleTSP.hs +112/−0
• MetaheuristicsDemo/test.hs +73/−0
• Setup.hs +2/−0
• metaheuristics.cabal +31/−0

@@ -0,0 +1,207 @@+{-# LANGUAGE MultiParamTypeClasses, AllowAmbiguousTypes #-}++module Control.Metaheuristics(+  HasQuality,quality,+  -- * Termination and post processing+  takeWhileProperty,+  -- * Execution of search processes+  openStreamMapArrow,+  executeMetaheuristic,+  -- * Simple lifting and injection+  mapArrow,+  inject,+  -- * General stream arrow combinators+  chunk,+  dechunk,+  stretch,+  doMany,+  delay,+  window,+  fanAll,+  newArrow,+  -- * Selection and filtering+  improvingFilter,+  improving,+  selectBest,+  selectRandom,+  diffFilter,+  -- * Escape Strategies+  escapeStrategy,+  replace,+  restart,+  uniformChoice,+  +  -- * SA cooling strategies+  logCooling,+  linCooling,+  geoCooling,+  +  -- * Metaheuristic algorithms+  iterativeImprover,+  stochasticIterativeImprover,+  simpleTABU,+  tabu,+  mergeStochasticAndTemperature,+  simulatedAnnealing,+  breed1,ga,+  populationPattern,degrade,aco+  ) where++import Control.Arrow+import Data.Stream (Stream(..),(<:>))+import qualified Data.Stream as S+import Control.Arrow.Transformer.Stream (StreamArrow(..),StreamMap(..))+import System.Random+import qualified Data.List as L++class Num v=>HasQuality f v where+  quality :: f->v++takeWhileProperty :: ([a]->Bool)->Int ->Stream a->[a]+takeWhileProperty term sz input = twi' input+  where+    -- twi' :: Stream a->[a]+    twi' xs = if term (S.take sz xs)+                then S.take sz xs+                else (S.head xs) : twi' (S.tail xs)+++openStreamMapArrow :: StreamMap a b->Stream a->Stream b+openStreamMapArrow (StreamArrow f) = f++executeMetaheuristic :: StreamArrow (->) b b->[b]->Stream b +executeMetaheuristic a x = let xs = S.prefix x (openStreamMapArrow a xs) in xs++newArrow :: (Stream a->Stream b)->StreamMap a b+newArrow = StreamArrow++mapArrow :: (a->b)->StreamMap a b+mapArrow f = StreamArrow (S.map f)++inject :: Stream a->(a->b->c)->StreamMap b c+inject s f = StreamArrow (S.zipWith f s)++chunk :: Int->StreamMap a [a]+chunk n = StreamArrow (f ) +  where f ys = S.take n ys `S.Cons` (f $ S.drop n ys)+++dechunk :: StreamMap [a] a+dechunk = StreamArrow (S.fromList . concat. S.toList )+++stretch:: Int->StreamMap sol sol+stretch n = StreamArrow +  (S.fromList  . concatMap (replicate n) .    S.toList )+++doMany :: Int->StreamMap sol sol'->StreamMap sol sol'+doMany n f = stretch n >>> f++delay::x->StreamMap x x+delay x=StreamArrow(S.Cons x)++window :: Int->StreamMap sol [sol]+window n=StreamArrow(\xs->S.scan f [] xs)+  where f qu x=if length qu==n then f (tail qu) x else qu++ [x]++fanAll :: [StreamMap a b]->StreamMap a [b]+fanAll fs = StreamArrow (\xs->S.fromList $ L.transpose [S.toList (f xs) | f<-map openStreamMapArrow fs       ])++improvingFilter :: Ord a=>StreamMap ([a],a) [a] +improvingFilter = StreamArrow $ S.map (\(xs,x)->filter (<x) xs)+++improving :: Ord a=>StreamMap a [a]->StreamMap a [a]+improving nF = nF &&& (StreamArrow id)  >>> improvingFilter++historyFilter :: Eq a => StreamMap ([a],[a]) [a]+historyFilter = mapArrow (\(n,h)->n L.\\ h)++selectBest :: Ord a=>StreamMap  [a] a +selectBest = StreamArrow $ S.map minimum++selectRandom :: RandomGen g=>g->StreamMap [a] a+selectRandom seedG = StreamArrow (f seedG)+  where f g xss = let xs = S.head xss+                      (i,g') = randomR (0,length xs-1) g+                  in xs !! i <:> f g' (S.tail xss)+++replace :: Stream sol ->StreamMap sol sol+replace supply = StreamArrow (const supply)++escapeStrategy :: StreamMap sol (Either sol sol)->StreamMap sol sol->StreamMap sol sol->StreamMap sol sol+escapeStrategy escapeTest s1 s2 = escapeTest >>> (s1 ||| s2)++restart :: StreamMap sol (Either sol sol)->StreamMap sol sol->Stream sol->StreamMap sol sol+restart restartTest s1 solSupply = escapeStrategy restartTest s1 (replace solSupply)++iterativeImprover :: Ord sol=>StreamMap sol [sol]->StreamMap sol sol+iterativeImprover neighbourhoodArrow = improving neighbourhoodArrow >>> selectBest++stochasticIterativeImprover :: (Ord sol,RandomGen g)=>g->StreamMap sol [sol]->StreamMap sol sol+stochasticIterativeImprover g neighbourhoodArrow = improving neighbourhoodArrow >>> selectRandom g++simpleTABU :: Ord sol=>StreamMap sol [sol]->Int->StreamMap sol sol +simpleTABU nF n = nF &&& (window n) >>> historyFilter >>> selectBest++tabu :: Ord sol=>StreamMap sol [sol]->Int->StreamMap sol sol +tabu nF n = fanAll [ improving nF,+                     nF &&& window n >>> historyFilter,+                     nF] >>> mapArrow concat >>> mapArrow head++mergeStochasticAndTemperature:: Floating v=>S.Stream v -> S.Stream v -> S.Stream v+mergeStochasticAndTemperature = S.zipWith (\r t ->1.0/t - log r)++--diffChoice :: (Num a, Ord a, HasQuality f) => a -> (f, f) -> f+--diffChoice r (a,b) = if r >= quality a - quality b then b else a++--sa :: HasQuality sol => StreamMap sol sol->S.Stream Double->S.Stream Double -> StreamMap sol sol+--sa mutate rs ts = (StreamArrow id)&&&mutate >>> inject (mergeStochasticAndTemperature rs ts) diffChoice++diffFilter :: (Num a, Ord a, HasQuality f a) => a -> (f, [f]) -> [f]+diffFilter r (s,xs) = [x | x<-xs, r >= quality s - quality x]++simulatedAnnealing :: (Floating v,Num v,Ord v, Ord sol, HasQuality sol v) =>StreamMap sol [sol] -> S.Stream v -> S.Stream v -> StreamMap sol sol+simulatedAnnealing neighbourhood rs ts = fanAll [ improving neighbourhood,+                                                  (StreamArrow id)&&&neighbourhood >>> inject (mergeStochasticAndTemperature rs ts) diffFilter,+                                                  mapArrow (:[]) ] >>> mapArrow concat >>> mapArrow head++{-| A logarithmic cooling strategy intended for use within simulated annealing. Broadly the first value is +    the starting temperature and the second a value between 0 and 1. -}+logCooling :: Floating b=>b->b->[b]+logCooling c d = map (\t->c / (log (t + d))) (iterate (+1) 1)++{-| The most common cooling strategy for simulated annealing, geometric. The first value is the starting temperature, +    the second a value between 0 and 1, the cooling rate.  -}+geoCooling :: Floating b=>b->b->[b]+geoCooling startTemp tempChange = iterate (* tempChange) startTemp++{-| Included for completeness, this is a cooling strategy for simulated annealing that is usually not very effective,+    a linear changing strategy. The first value is the starting temperature the second is the value to increase it by +    at each step. In order to have it reduce at each step, pass a negative value. +-}+linCooling :: Floating b=>b->b->[b]+linCooling startTemp tempChange= iterate (+ tempChange) startTemp++breed1 :: RandomGen g=>StreamMap [sol] sol->g->StreamMap [sol] sol+breed1 recombine g1 = doMany 2 (selectRandom g1) >>> chunk 2 >>> recombine++ga :: Int ->StreamMap sol (Either sol sol)->StreamMap sol sol ->StreamMap [sol] sol->StreamMap sol sol+ga popSize mutateTest mutate breed = chunk popSize >>> doMany popSize breed >>> mutateTest >>> (StreamArrow id ||| mutate)+++uniformChoice :: RandomGen g=>Float->g->StreamMap sol (Either sol sol)+uniformChoice chance g = StreamArrow (S.fromList . f (randoms g) . S.toList)+  where f (x:xs) (s:sols) | x<chance = Left s:f xs sols+                          | otherwise = Right s : f xs sols++populationPattern::Int->(sol->pat)->StreamMap [pat] pat-> StreamMap sol pat +populationPattern popSize createPattern mergePattern=mapArrow createPattern>>>chunk popSize>>>mergePattern>>>stretch popSize++degrade :: StreamMap a a -> StreamMap (a,a) a->StreamMap a a +degrade df mf = loop (second df >>> mf >>> (StreamArrow id &&& StreamArrow id))++aco :: pat->Int->(sol -> pat)->StreamMap [pat] pat ->StreamMap (pat, pat) pat->StreamMap pat pat ->StreamMap pat sol->StreamMap sol sol+aco defaultPattern popsize cfp mergePopulation mergePair df createsolution = mapArrow cfp >>> chunk popsize >>> mergePopulation >>> degrade (delay defaultPattern >>> df ) mergePair >>> stretch popsize >>> createsolution

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@@ -0,0 +1,112 @@+module SimpleTSP where++import qualified Data.Map as M+import qualified System.Random as R++cityCoordinates :: [(Float,Float)]+cityCoordinates = [(8.39816197926126, 15.185014516132743), (5.805977666331062, 8.023032216008712), (16.714739233303042, 13.92861301975959), (17.78946373699672, 4.015830245456648), (14.957102430197795, 5.872523608300993), (3.9898709586837433, 8.971759629020063), (8.293182956694752, 15.09786669714841), (18.350802237395172, 4.276634196229338), (16.816723540931694, 1.7317842244869075), (1.8717215787226338, 0.88215954938345)]++euclideanDistance :: (Float,Float)->(Float,Float)->Float+euclideanDistance (x1,y1) (x2,y2) = sqrt $ (x1-x2)*(x1-x2)+(y1-y2)*(y1-y2)++cityDistances :: M.Map (Int,Int) Float+cityDistances = M.fromList [((a,b),d a b) | a<-cities,b<-cities  ]+  where cities = [0..length cityCoordinates-1]+        d i1 i2 = euclideanDistance (cityCoordinates !! i1) (cityCoordinates !! i2) ++edgeLength x = cityDistances M.! x++data TSP = TSP [Int] Float deriving (Show,Eq)++type TSPPAT = M.Map (Int,Int) Float+++instance Ord TSP where+  compare (TSP _ a) (TSP _ b) = compare a b+  ++++numCities (TSP x _) = length x++mkTSP :: [Int]->TSP+mkTSP x = TSP x (tspValue x)++tspValue :: [Int]->Float+tspValue ts = sum $ map edgeLength edges+  where edges = (last ts,0): zip (0:ts) ts+                 +tspToPat :: TSP->TSPPAT+tspToPat (TSP ts _) = M.fromList (simPat edges)+  where edges = (last ts,0): zip (0:ts) ts+        val = sum $ map edgeLength edges+        simPat [] = []+        simPat ((a,b):xs) = ((a,b),e):((b,a),e):simPat xs+          where e=val/edgeLength (a,b)  ++swapCities :: Int->Int->TSP->TSP+swapCities i1 i2 (TSP x _) = mkTSP (zipWith f x [0..])+  where f c i | i==i1 = x !! i2+              | i==i2 = x !! i1+              | otherwise = c++adjacentNeighbourhood :: TSP->[TSP]+adjacentNeighbourhood x = [swapCities a b x |   (a,b)<- zip ps (tail ps)]+  where ps = [0..numCities x-1]++nullPat :: TSPPAT+nullPat = M.empty++degradePat :: TSPPAT->TSPPAT+degradePat t = M.mapMaybe   f    t+  where f a | a<0.01 = Nothing+            | otherwise = Just (a*0.9)  ++mergePat :: TSPPAT->TSPPAT->TSPPAT+mergePat a b = M.unionWith (+) a b++mergePats :: [TSPPAT]->TSPPAT+mergePats (x:xs) = foldl mergePat x xs++createSol1 :: R.RandomGen g=>g->TSPPAT->TSP+createSol1 g p = mkTSP (f 0 [1..length cityCoordinates-1] (R.randoms g))+  where f currentCity [] _ = []+        f currentCity cityOpts (r:rs) = let vs = map (weight currentCity) cityOpts+                                            s=sum vs * r+                                            (xs,x,ys) = finder s (zip vs cityOpts)+                                        in x:f x (xs++ys) rs+        finder finalScore ((value,city):others) | value<finalScore = (city:xs,x,ys)+                                                | otherwise = ([],city,map snd others)+          where (xs,x,ys) = finder (finalScore-value) others                                      +        weight c k = M.findWithDefault 1.0 (c,k) p ++createSol :: R.RandomGen g=>g->TSPPAT->(TSP,g)+createSol g p = let (g1,g2) = R.split g in (createSol1 g1 p,g2)++createSolutions g (p:pats) = let (a,b) = createSol g p in a:createSolutions b pats++randomSolution :: R.RandomGen g=>g->(TSP,g)+randomSolution g = createSol g nullPat++randomSolutions :: R.RandomGen g=>g->[TSP]+randomSolutions g = let (a,b) = randomSolution g in a:randomSolutions b++recombineSolutions :: R.RandomGen g=>g->[TSP]->(TSP,g)+recombineSolutions g xs = createSol g (mergePats ps)+  where ps= map tspToPat xs+      +++keyRandGen = R.mkStdGen 52++++{- iteratedSols = f nullPat keyRandGen+  where f p g = let (g1,g2) = R.split g+                    s = createSol1 g1 p+                    p2=mergePat (degradePat p) (tspToPat s)+                in s:f p2 g2++test= Prelude.map tspValue $ take 10 $ iteratedSols  -}++

@@ -0,0 +1,73 @@+{-# LANGUAGE MultiParamTypeClasses,TypeSynonymInstances,FlexibleInstances  #-}++import Control.Metaheuristics+import System.Random+import qualified Data.Stream as S+import qualified SimpleTSP as TSP++import Control.Arrow++instance HasQuality TSP.TSP Float where+  quality (TSP.TSP _ x)= x ++++displaySeries :: [TSP.TSP]->IO()+displaySeries [] = print "END"+displaySeries (t:tsps) = do print t+                            displaySeries tsps++-- TEST Hill climbers+testHillClimber = displaySeries $ S.take 100 $ executeMetaheuristic (iterativeImprover (mapArrow TSP.adjacentNeighbourhood  )) [s]    +  where +    (s,g)=TSP.randomSolution (TSP.keyRandGen)+   +testStochasticClimber = displaySeries $ S.take 100 $ executeMetaheuristic (stochasticIterativeImprover g (mapArrow TSP.adjacentNeighbourhood  )) [s]    +  where +    (s,g)=TSP.randomSolution (TSP.keyRandGen) ++-- TEST hill climber + escape+-- Not efficient. We create the neighbourhood twice and make sure it will be valid in the second generation+testRestart = displaySeries $ S.take 100 $ executeMetaheuristic (escapeStrategy  esTest  (stochasticIterativeImprover g2 neiArrow )  (replace (S.fromList ss) ) ) [s]+  where+    (g1,g2) = split TSP.keyRandGen+    (s:ss)=TSP.randomSolutions g1+    neiArrow = mapArrow TSP.adjacentNeighbourhood  +    esTest = mapArrow f+    f s | length ns == 0 = Right s+        | otherwise = Left s+     where ns = [ p       |    p<-  TSP.adjacentNeighbourhood s,p<s]+++-- TEST TABU+testTABU = displaySeries $ S.take 100 $ executeMetaheuristic (tabu (mapArrow TSP.adjacentNeighbourhood) 5 ) [s]    +  where +    (s,g)=TSP.randomSolution (TSP.keyRandGen) +    +-- TEST SA+testSA = displaySeries $ S.take 100 $ executeMetaheuristic (simulatedAnnealing neiArrow rs ts) [s]+  where ts = S.fromList (logCooling 100 0.5 :: [Float])+        (s,g)=TSP.randomSolution (TSP.keyRandGen) +        rs = S.fromList (randoms g :: [Float])+        neiArrow = mapArrow TSP.adjacentNeighbourhood  ++-- TEST GA+testGA = displaySeries $ S.take 100 $ executeMetaheuristic (ga 10 mutateTest mutate  b) initialSols+  where (g1,g2) = split TSP.keyRandGen+        (g3,g4) = split g2+        (g5,g6) = split g3+        initialSols = take 10 $ TSP.randomSolutions g5+        b = breed1 (newArrow (wrappedRecombine g1)) g2 +        wrappedRecombine g xss = let (a,b) = TSP.recombineSolutions g (S.head xss) in S.Cons a (wrappedRecombine b (S.tail xss))+        mutateTest = uniformChoice (0.1) g3+        mutate = replace (S.fromList $ TSP.randomSolutions g6)++-- TEST ACO+testACO = displaySeries $ S.take 100 $ executeMetaheuristic (aco TSP.nullPat 10 TSP.tspToPat (mapArrow TSP.mergePats) mergePair (mapArrow TSP.degradePat) createsolution) initialSols+  where +    mergePair = mapArrow (uncurry TSP.mergePat)+    (g1,g2) = split TSP.keyRandGen+    initialSols = take 10 $ TSP.randomSolutions g1+    createsolution = newArrow (S.fromList . TSP.createSolutions g2 . S.toList)++

@@ -0,0 +1,2 @@+import Distribution.Simple+main=defaultMain

@@ -0,0 +1,31 @@+Name:              metaheuristics+Version:           0.0.8+Synopsis:          Generalised local search within Haskell, for applications in combinatorial optimisation. +Description:       This library operates by representing metaheuristics as generators of solutions, or +                   streams of solutions, which are themselves the result of resolving the interactions of +                   other streams of values. The library contains combinators for constructing and +                   managing these structures.     +Stability:         experimental+Category:          Control, Optimisation, Local Search+Author:            Richard Senington & David Duke+License:           GPL+license-file:      LICENSE+Copyright:         Copyright (c) 2013 Richard Senington+Homepage:          +Maintainer:        sc06r2s@leeds.ac.uk+Build-Type:        Simple+Cabal-Version:     >= 1.2+Data-files:        ++library+  Exposed-Modules: Control.Metaheuristics+                   +  GHC-Options:     -O2 +  Build-Depends:   base <=4.8.2.0,+                   Stream >= 0.4.7.2,+                   arrows >= 0.4.4.2,+                   random >=1.1+                   +  +  extensions: MultiParamTypeClasses,+              FunctionalDependencies