Particle Swarm Optimization (See related posts)

# Particle swarm optimization
# Maxime Biais : <http://www.biais.org/blog/>

from random import uniform
 
class PSO:
    def __init__(self, pop_size, min, max, phi, phi2, lr, maxiter, func):
        self.func = func
        self.pop = []
        # 0: position, 1: velocity, 2: fitness
        self.min = min
        self.max = max
        for i in xrange(pop_size):
            self.pop.append([uniform(self.min, self.max), 
                                   uniform(-1, 1), 0])
        self.evaluate()
        self.gdest = self.pop[0]
        self.pdest = self.pop[0]
        self.phi = phi
        self.phi2 = phi2
        self.lr = lr
        self.maxiter = maxiter
    
    def update_velocity(self):
        for i in self.pop:
            i[1] = self.lr * i[1] + uniform(0, self.phi) \
                    * (self.pdest[0] - i[0]) + uniform(0, self.phi2) \
                    * (self.gdest[0] - i[0])
 
    def evaluate(self):
        for i in self.pop:
            i[2] = self.func(i[0])
 
    def move(self):
        for i in self.pop:
            i[0] += i[1]
 
    def __cmp_by_fitness(self, a, b):
        return cmp(a[2], b[2])
    
    def run(self, update_func=False):
        for i in xrange(self.maxiter):
            if update_func:
                update_func()
            self.update_velocity()
            self.move()
            self.evaluate()
            self.pop.sort(self.__cmp_by_fitness, reverse=0)
            self.pdest = self.pop[0]
            if self.pdest[2] < self.gdest[2]:
                self.gdest = self.pdest