From 45d164645b3e10ce305d859e1474e3fd81150b9c Mon Sep 17 00:00:00 2001
From: trollhase <valentingehrke@gmx.de>
Date: Sun, 29 Sep 2013 06:57:16 +0200
Subject: [PATCH] Particle Swarm Optimization added.

---
 pso.py | 135 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 135 insertions(+)
 create mode 100644 pso.py

diff --git a/pso.py b/pso.py
new file mode 100644
index 0000000..5da195e
--- /dev/null
+++ b/pso.py
@@ -0,0 +1,135 @@
+from random import random
+from math import sqrt
+
+def product(it,key=lambda x: x):
+	p = 1
+	for n in map(key,it):
+		p*=n
+	return n
+
+class Vector(list):
+	def __add__(self,other):
+		if len(self) != len(other):
+			raise Exception("Can't add vectors of different size: %d %d" % (len(self), len(other)))
+		return Vector([ self[i]+other[i] for i in range(len(self)) ])
+
+	def __sub__(self,other):
+		if len(self) != len(other):
+			raise Exception("Can't subtract vectors of different size")
+		return Vector([ self[i]-other[i] for i in range(len(self)) ])
+
+	def __mul__(self,factor):
+		return Vector([ self[i]*factor for i in range(len(self)) ])
+
+	def __div__(self,factor):
+		return self.__mul__(1.0/factor)
+
+	def __rmul__(self,factor):
+		return self.__mul__(factor)
+
+	def __rdiv__(self,factor):
+		raise NotImplemented
+
+	def __iadd__(self,other):
+		return self.__add__(other)
+
+	def __isub__(self,other):
+		return self.__sub__(other)
+
+	def __neg__(self):
+		return self.__mul__(-1.0)
+
+	def __setitem__(self,k,v):
+		raise Exception("Not available") # immutable
+
+	def distance(self, other):
+		if len(self) != len(other):
+			raise Exception("Can't calculate distance between vectors of different size")
+		return sqrt(sum([ (self[i]-other[i])**2 for i in range(len(self)) ]))
+
+class Particle:
+	def __init__(self,position,velocity):
+		self.gsize = len(position)
+		self.position = Vector(position)
+		self.bestposition = Vector(position[:])
+		self.velocity = Vector(velocity)
+
+class ParticleCreator:
+	def __init__(self,searchspace):
+		self.searchspace = searchspace
+
+	def create(self):
+		return Particle([ (random()*(r[1]-r[0]) + r[0]) for r in self.searchspace ],
+			[0 for i in range(len(self.searchspace))])
+
+class Fitness:
+	targets = [Vector([0,0])]
+
+	def fitness(self,position):
+		return min([ position.distance(target) for target in self.targets ])
+
+class Swarm:
+	def __init__(self, n, creator, fitness, inertiaWeight, cog1, cog2):
+		self.particles = map(lambda x: creator.create(), range(n))
+		self.inertiaWeight = inertiaWeight
+		self.cog1 = cog1
+		self.cog2 = cog2
+		self.fitness = fitness
+
+	def bestOfSwarm(self):
+		return min( map(lambda p: p.bestposition, self.particles), key=lambda position: self.fitness.fitness(position) )
+
+	def __bestNeighbor(self,particle, distance=20):
+		#distance = max(min( self.particles, key=lambda p: particle.position.distance(p.position) ),distance)
+		#return min( map(lambda p: p.bestposition, filter(lambda p: particle.position.distance(p.position) <= distance, self.particles) ), key=lambda position: self.fitness.fitness(position))
+		i = self.particles.index(particle)
+		l = (i+1) % len(self.particles)
+		r = (i-1) % len(self.particles)
+		return min( map(lambda p: p.bestposition, [self.particles[l], self.particles[r], particle]), key=lambda position: self.fitness.fitness(position) )
+
+
+	def __updatePosition(self):
+		for p in self.particles:
+			new_position = p.position + p.velocity
+			if self.fitness.fitness(new_position) < self.fitness.fitness(p.position):
+				p.bestposition = new_position
+			p.position = new_position
+
+	def __updateVelocity(self):
+		bestOfSwarm = self.bestOfSwarm()
+		for p in self.particles:
+			#bestNeighbor = self.__bestNeighbor(p)
+			m = p.velocity*self.inertiaWeight + (p.bestposition-p.position)*(self.cog1*random()) + (bestOfSwarm-p.position)*(self.cog2*random())
+			p.velocity = m
+
+	def step(self):
+		self.__updateVelocity()
+		self.__updatePosition()
+
+	def stepN(self,n):
+		for i in range(n):
+			self.step()
+
+	def runUntilSwarmSize(self,sizeMax=1.0):
+		iterations = 0
+		while self.__swarmSize() > sizeMax:
+			self.step()
+			iterations += 1
+		return iterations
+
+
+	def __centerOfSwarm(self):
+		return reduce(lambda a,b: a+b, [ p.position for p in self.particles ], Vector([0,0]) )/len(self.particles)
+
+	def __swarmSize(self):
+		center = self.__centerOfSwarm()
+		return max([ p.position.distance(center) for p in self.particles ])
+
+if __name__ == '__main__':
+	swarm = Swarm( 20, ParticleCreator([(-2000.0,2000.0),(-2000.0,2000.0)]), Fitness(), 1, 0.4, 0.2 )
+	
+	iterations = swarm.runUntilSwarmSize(1)
+
+	print "%d iterations" % iterations
+
+	print swarm.bestOfSwarm()