#! /usr/bin/env python
# Copyright (c) 2014 KU Leuven, ESAT-STADIUS
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import math
import operator as op
import random
import array
import functools
from .solver_registry import register_solver
from .util import Solver, _copydoc, uniform_in_bounds
@register_solver('particle swarm',
'particle swarm optimization',
['Maximizes the function using particle swarm optimization.',
' ',
'This is a two-phase approach:',
'1. Initialization: randomly initializes num_particles particles.',
' Particles are randomized uniformly within the box constraints.',
'2. Iteration: particles move during num_generations iterations.',
' Movement is based on their velocities and mutual attractions.',
' ',
'This function requires the following arguments:',
'- num_particles: number of particles to use in the swarm',
'- num_generations: number of iterations used by the swarm',
'- max_speed: maximum speed of the particles in each direction (in (0, 1])',
'- box constraints via key words: constraints are lists [lb, ub]', ' ',
'This solver performs num_particles*num_generations function evaluations.'
])
[docs]class ParticleSwarm(Solver):
"""
.. include:: /global.rst
Please refer to |pso| for details on this algorithm.
"""
[docs] class Particle:
def __init__(self, position, speed, best, fitness, best_fitness):
"""Constructs a Particle."""
self.position = position
self.speed = speed
self.best = best
self.fitness = fitness
self.best_fitness = best_fitness
[docs] def clone(self):
"""Clones this Particle."""
return ParticleSwarm.Particle(position=self.position[:], speed=self.speed[:],
best=self.best[:], fitness=self.fitness,
best_fitness=self.best_fitness)
def __str__(self):
string = 'Particle{position=' + str(self.position)
string += ', speed=' + str(self.speed)
string += ', best=' + str(self.best)
string += ', fitness=' + str(self.fitness)
string += ', best_fitness=' + str(self.best_fitness)
string += '}'
return string
def __init__(self, num_particles, num_generations, max_speed=None, phi1=2.0, phi2=2.0, **kwargs):
"""
Initializes a PSO solver.
:param num_particles: number of particles to use
:type num_particles: int
:param num_generations: number of generations to use
:type num_generations: int
:param max_speed: maximum velocity of each particle
:type max_speed: float or None
:param phi1: parameter used in updating position based on local best
:type phi1: float
:param phi2: parameter used in updating position best on global best
:type phi2: float
:param kwargs: box constraints for each hyperparameter
:type kwargs: {'name': [lb, ub], ...}
The number of function evaluations it will perform is `num_particles`*`num_generations`.
The search space is rescaled to the unit hypercube before the solving process begins.
>>> solver = ParticleSwarm(num_particles=10, num_generations=5, x=[-1, 1], y=[0, 2])
>>> solver.bounds['x']
[-1, 1]
>>> solver.bounds['y']
[0, 2]
>>> solver.num_particles
10
>>> solver.num_generations
5
.. warning:: |warning-unconstrained|
"""
assert all([len(v) == 2 and v[0] <= v[1]
for v in kwargs.values()]), 'kwargs.values() are not [lb, ub] pairs'
self._bounds = kwargs
self._num_particles = num_particles
self._num_generations = num_generations
if max_speed is None:
max_speed = 0.5 / num_generations
self._max_speed = max_speed
self._smax = [self.max_speed * (b[1] - b[0])
for _, b in self.bounds.items()]
self._smin = list(map(op.neg, self.smax))
self._phi1 = phi1
self._phi2 = phi2
@property
[docs] def phi1(self):
return self._phi1
@property
[docs] def phi2(self):
return self._phi2
@staticmethod
[docs] def suggest_from_box(num_evals, **kwargs):
"""Create a configuration for a ParticleSwarm solver.
:param num_evals: number of permitted function evaluations
:type num_evals: int
:param kwargs: box constraints
:type kwargs: {'param': [lb, ub], ...}
>>> config = ParticleSwarm.suggest_from_box(200, x=[-1, 1], y=[0, 1])
>>> config['x']
[-1, 1]
>>> config['y']
[0, 1]
>>> config['num_particles'] > 0
True
>>> config['num_generations'] > 0
True
>>> solver = ParticleSwarm(**config)
>>> solver.bounds['x']
[-1, 1]
>>> solver.bounds['y']
[0, 1]
"""
d = dict(kwargs)
if num_evals > 1000:
d['num_particles'] = 100
d['num_generations'] = int(math.ceil(float(num_evals) / 100))
elif num_evals >= 200:
d['num_particles'] = 40
d['num_generations'] = int(math.ceil(float(num_evals) / 40))
elif num_evals >= 10:
d['num_particles'] = 10
d['num_generations'] = int(math.ceil(float(num_evals) / 10))
else:
d['num_particles'] = num_evals
d['num_generations'] = 1
return d
@property
[docs] def num_particles(self):
return self._num_particles
@property
[docs] def num_generations(self):
return self._num_generations
@property
[docs] def max_speed(self):
return self._max_speed
@property
[docs] def smax(self):
return self._smax
@property
[docs] def smin(self):
return self._smin
@property
[docs] def bounds(self):
return self._bounds
[docs] def generate(self):
"""Generate a new Particle."""
part = ParticleSwarm.Particle(position=array.array('d', uniform_in_bounds(self.bounds)),
speed=array.array('d', map(random.uniform,
self.smin, self.smax)),
best=None, fitness=None, best_fitness=None)
return part
[docs] def updateParticle(self, part, best, phi1, phi2):
"""Update the particle."""
u1 = (random.uniform(0, phi1) for _ in range(len(part.position)))
u2 = (random.uniform(0, phi2) for _ in range(len(part.position)))
v_u1 = map(op.mul, u1,
map(op.sub, part.best, part.position))
v_u2 = map(op.mul, u2,
map(op.sub, best.position, part.position))
part.speed = array.array('d', map(op.add, part.speed,
map(op.add, v_u1, v_u2)))
for i, speed in enumerate(part.speed):
if speed < self.smin[i]:
part.speed[i] = self.smin[i]
elif speed > self.smax[i]:
part.speed[i] = self.smax[i]
part.position[:] = array.array('d', map(op.add, part.position, part.speed))
[docs] def particle2dict(self, particle):
return dict([(k, v) for k, v in zip(self.bounds.keys(),
particle.position)])
@_copydoc(Solver.optimize)
[docs] def optimize(self, f, maximize=True, pmap=map):
@functools.wraps(f)
def evaluate(d):
return f(**d)
if maximize:
fit = 1.0
else:
fit = -1.0
pop = [self.generate() for _ in range(self.num_particles)]
best = None
for g in range(self.num_generations):
fitnesses = pmap(evaluate, list(map(self.particle2dict, pop)))
for part, fitness in zip(pop, fitnesses):
part.fitness = fit*fitness
if not part.best or part.best_fitness < part.fitness:
part.best = part.position
part.best_fitness = part.fitness
if not best or best.fitness < part.fitness:
best = part.clone()
for part in pop:
self.updateParticle(part, best, self.phi1, self.phi2)
return dict([(k, v)
for k, v in zip(self.bounds.keys(), best.position)]), None
