# -*- coding: utf-8 -*-
"""
A Ring Network Topology
This class implements a ring topology. In this topology,
the particles are connected with their k nearest neighbors.
This social behavior is often found in LocalBest PSO
optimizers.
"""
# Import standard library
import logging
# Import modules
import numpy as np
from scipy.spatial import cKDTree
from .. import operators as ops
from ..handlers import BoundaryHandler, VelocityHandler
from ...utils.reporter import Reporter
from .base import Topology
[docs]class Ring(Topology):
[docs] def __init__(self, static=False):
"""Initializes the class
Parameters
----------
static : bool (Default is :code:`False`)
a boolean that decides whether the topology
is static or dynamic
"""
super(Ring, self).__init__(static)
self.rep = Reporter(logger=logging.getLogger(__name__))
[docs] def compute_gbest(self, swarm, p, k, **kwargs):
"""Update the global best using a ring-like neighborhood approach
This uses the cKDTree method from :code:`scipy` to obtain the nearest
neighbors.
Parameters
----------
swarm : pyswarms.backend.swarms.Swarm
a Swarm instance
p: int {1,2}
the Minkowski p-norm to use. 1 is the
sum-of-absolute values (or L1 distance) while 2 is
the Euclidean (or L2) distance.
k : int
number of neighbors to be considered. Must be a
positive integer less than :code:`n_particles`
Returns
-------
numpy.ndarray
Best position of shape :code:`(n_dimensions, )`
float
Best cost
"""
try:
# Check if the topology is static or not and assign neighbors
if (self.static and self.neighbor_idx is None) or not self.static:
# Obtain the nearest-neighbors for each particle
tree = cKDTree(swarm.position)
_, self.neighbor_idx = tree.query(swarm.position, p=p, k=k)
# Map the computed costs to the neighbour indices and take the
# argmin. If k-neighbors is equal to 1, then the swarm acts
# independently of each other.
if k == 1:
# The minimum index is itself, no mapping needed.
self.neighbor_idx = self.neighbor_idx[:, np.newaxis]
best_neighbor = np.arange(swarm.n_particles)
else:
idx_min = swarm.pbest_cost[self.neighbor_idx].argmin(axis=1)
best_neighbor = self.neighbor_idx[
np.arange(len(self.neighbor_idx)), idx_min
]
# Obtain best cost and position
best_cost = np.min(swarm.pbest_cost[best_neighbor])
best_pos = swarm.pbest_pos[best_neighbor]
except AttributeError:
self.rep.logger.exception(
"Please pass a Swarm class. You passed {}".format(type(swarm))
)
raise
else:
return (best_pos, best_cost)
[docs] def compute_velocity(
self,
swarm,
clamp=None,
vh=VelocityHandler(strategy="unmodified"),
bounds=None,
):
"""Compute the velocity matrix
This method updates the velocity matrix using the best and current
positions of the swarm. The velocity matrix is computed using the
cognitive and social terms of the swarm.
A sample usage can be seen with the following:
.. code-block :: python
import pyswarms.backend as P
from pyswarms.backend.swarm import Swarm
from pyswarms.backend.handlers import VelocityHandler
from pyswarms.backend.topology import Ring
my_swarm = P.create_swarm(n_particles, dimensions)
my_topology = Ring(static=False)
my_vh = VelocityHandler(strategy="invert")
for i in range(iters):
# Inside the for-loop
my_swarm.velocity = my_topology.update_velocity(my_swarm, clamp, my_vh,
bounds)
Parameters
----------
swarm : pyswarms.backend.swarms.Swarm
a Swarm instance
clamp : tuple of floats (default is :code:`None`)
a tuple of size 2 where the first entry is the minimum velocity
and the second entry is the maximum velocity. It
sets the limits for velocity clamping.
vh : pyswarms.backend.handlers.VelocityHandler
a VelocityHandler instance
bounds : tuple of :code:`np.ndarray` or list (default is :code:`None`)
a tuple of size 2 where the first entry is the minimum bound while
the second entry is the maximum bound. Each array must be of shape
:code:`(dimensions,)`.
Returns
-------
numpy.ndarray
Updated velocity matrix
"""
return ops.compute_velocity(swarm, clamp, vh, bounds)
[docs] def compute_position(
self, swarm, bounds=None, bh=BoundaryHandler(strategy="periodic")
):
"""Update the position matrix
This method updates the position matrix given the current position and
the velocity. If bounded, it waives updating the position.
Parameters
----------
swarm : pyswarms.backend.swarms.Swarm
a Swarm instance
bounds : tuple of :code:`np.ndarray` or list (default is :code:`None`)
a tuple of size 2 where the first entry is the minimum bound while
the second entry is the maximum bound. Each array must be of shape
:code:`(dimensions,)`.
bh : pyswarms.backend.handlers.BoundaryHandler
a BoundaryHandler instance
Returns
-------
numpy.ndarray
New position-matrix
"""
return ops.compute_position(swarm, bounds, bh)