# encoding=utf8
import logging
import math
import numpy as np
from niapy.algorithms.algorithm import Algorithm
logging.basicConfig()
logger = logging.getLogger('niapy.algorithms.basic')
logger.setLevel('INFO')
__all__ = ['CatSwarmOptimization']
[docs]class CatSwarmOptimization(Algorithm):
r"""Implementation of Cat swarm optimization algorithm.
**Algorithm:** Cat swarm optimization
**Date:** 2019
**Author:** Mihael Baketarić
**License:** MIT
**Reference paper:** Chu, S. C., Tsai, P. W., & Pan, J. S. (2006). Cat swarm optimization.
In Pacific Rim international conference on artificial intelligence (pp. 854-858). Springer, Berlin, Heidelberg.
"""
Name = ['CatSwarmOptimization', 'CSO']
[docs] @staticmethod
def info():
r"""Get algorithm information.
Returns:
str: Algorithm information.
See Also:
* :func:`niapy.algorithms.Algorithm.info`
"""
return r"""Chu, S. C., Tsai, P. W., & Pan, J. S. (2006). Cat swarm optimization.
In Pacific Rim international conference on artificial intelligence (pp. 854-858).
Springer, Berlin, Heidelberg."""
[docs] def __init__(self, population_size=30, mixture_ratio=0.1, c1=2.05, smp=3, spc=True, cdc=0.85, srd=0.2,
max_velocity=1.9, *args, **kwargs):
"""Initialize CatSwarmOptimization.
Args:
population_size (int): Number of individuals in population.
mixture_ratio (float): Mixture ratio.
c1 (float): Constant in tracing mode.
smp (int): Seeking memory pool.
spc (bool): Self-position considering.
cdc (float): Decides how many dimensions will be varied.
srd (float): Seeking range of the selected dimension.
max_velocity (float): Maximal velocity.
See Also:
* :func:`niapy.algorithms.Algorithm.__init__`
"""
super().__init__(population_size, *args, **kwargs)
self.mixture_ratio = mixture_ratio
self.c1 = c1
self.smp = smp
self.spc = spc
self.cdc = cdc
self.srd = srd
self.max_velocity = max_velocity
[docs] def set_parameters(self, population_size=30, mixture_ratio=0.1, c1=2.05, smp=3, spc=True, cdc=0.85, srd=0.2,
max_velocity=1.9, **kwargs):
r"""Set the algorithm parameters.
Args:
population_size (int): Number of individuals in population.
mixture_ratio (float): Mixture ratio.
c1 (float): Constant in tracing mode.
smp (int): Seeking memory pool.
spc (bool): Self-position considering.
cdc (float): Decides how many dimensions will be varied.
srd (float): Seeking range of the selected dimension.
max_velocity (float): Maximal velocity.
See Also:
* :func:`niapy.algorithms.Algorithm.set_parameters`
"""
super().set_parameters(population_size, **kwargs)
self.mixture_ratio = mixture_ratio
self.c1 = c1
self.smp = smp
self.spc = spc
self.cdc = cdc
self.srd = srd
self.max_velocity = max_velocity
[docs] def get_parameters(self):
r"""Get parameters values of the algorithm.
Returns:
Dict[str, Any]: Algorithm parameters.
"""
params = super().get_parameters()
params.update({
'mixture_ratio': self.mixture_ratio,
'c1': self.c1,
'smp': self.smp,
'spc': self.spc,
'cdc': self.cdc,
'srd': self.srd,
'max_velocity': self.max_velocity
})
return params
[docs] def init_population(self, task):
r"""Initialize population.
Args:
task (Task): Optimization task.
Returns:
Tuple[numpy.ndarray, numpy.ndarray[float], Dict[str, Any]]:
1. Initialized population.
2. Initialized populations fitness/function values.
3. Additional arguments:
* Dictionary of modes (seek or trace) and velocities for each cat
See Also:
* :func:`niapy.algorithms.Algorithm.init_population`
"""
pop, fpop, d = super().init_population(task)
d['velocities'] = self.uniform(-self.max_velocity, self.max_velocity, (self.population_size, task.dimension))
return pop, fpop, d
[docs] def random_seek_trace(self):
r"""Set cats into seeking/tracing mode randomly.
Returns:
numpy.ndarray: One or zero. One means tracing mode. Zero means seeking mode. Length of list is equal to population_size.
"""
modes = np.zeros(self.population_size, dtype=np.int32)
indices = self.rng.choice(self.population_size, int(self.population_size * self.mixture_ratio), replace=False)
modes[indices] = 1
return modes
[docs] def weighted_selection(self, weights):
r"""Random selection considering the weights.
Args:
weights (numpy.ndarray): weight for each potential position.
Returns:
int: index of selected next position.
"""
cumulative_sum = np.cumsum(weights)
return np.argmax(cumulative_sum >= (self.random() * cumulative_sum[-1]))
[docs] def seeking_mode(self, task, cat, cat_fitness, pop, fpop, fxb):
r"""Seeking mode.
Args:
task (Task): Optimization task.
cat (numpy.ndarray): Individual from population.
cat_fitness (float): Current individual's fitness/function value.
pop (numpy.ndarray): Current population.
fpop (numpy.ndarray): Current population fitness/function values.
fxb (float): Current best cat fitness/function value.
Returns:
Tuple[numpy.ndarray, float, numpy.ndarray, float]:
1. Updated individual's position
2. Updated individual's fitness/function value
3. Updated global best position
4. Updated global best fitness/function value
"""
cat_copies = []
cat_copies_fs = []
for j in range(self.smp - 1 if self.spc else self.smp):
cat_copies.append(cat.copy())
indexes = np.arange(task.dimension)
self.rng.shuffle(indexes)
to_vary_indexes = indexes[:int(task.dimension * self.cdc)]
if self.integers(2) == 1:
cat_copies[j][to_vary_indexes] += cat_copies[j][to_vary_indexes] * self.srd
else:
cat_copies[j][to_vary_indexes] -= cat_copies[j][to_vary_indexes] * self.srd
cat_copies[j] = task.repair(cat_copies[j])
cat_copies_fs.append(task.eval(cat_copies[j]))
if self.spc:
cat_copies.append(cat.copy())
cat_copies_fs.append(cat_fitness)
cat_copies_select_probs = np.ones(len(cat_copies))
worst_fitness = np.max(cat_copies_fs)
best_fitness = np.min(cat_copies_fs)
if any(x != cat_copies_fs[0] for x in cat_copies_fs):
fb = worst_fitness
if math.isinf(fb):
cat_copies_select_probs = np.full(len(cat_copies), fb)
else:
cat_copies_select_probs = np.abs(cat_copies_fs - fb) / (worst_fitness - best_fitness)
if best_fitness < fxb:
ind = self.integers(self.population_size)
pop[ind] = cat_copies[np.where(cat_copies_fs == best_fitness)[0][0]]
fpop[ind] = best_fitness
sel_index = self.weighted_selection(cat_copies_select_probs)
return cat_copies[sel_index], cat_copies_fs[sel_index], pop, fpop
[docs] def tracing_mode(self, task, cat, velocity, xb):
r"""Tracing mode.
Args:
task (Task): Optimization task.
cat (numpy.ndarray): Individual from population.
velocity (numpy.ndarray): Velocity of individual.
xb (numpy.ndarray): Current best individual.
Returns:
Tuple[numpy.ndarray, float, numpy.ndarray]:
1. Updated individual's position
2. Updated individual's fitness/function value
3. Updated individual's velocity vector
"""
new_velocity = np.clip(velocity + (self.random(len(velocity)) * self.c1 * (xb - cat)),
-self.max_velocity, self.max_velocity)
cat_new = task.repair(cat + new_velocity)
return cat_new, task.eval(cat_new), new_velocity
[docs] def run_iteration(self, task, population, population_fitness, best_x, best_fitness, **params):
r"""Core function of Cat Swarm Optimization algorithm.
Args:
task (Task): Optimization task.
population (numpy.ndarray): Current population.
population_fitness (numpy.ndarray): Current population fitness/function values.
best_x (numpy.ndarray): Current best individual.
best_fitness (float): Current best cat fitness/function value.
**params (Dict[str, Any]): Additional function arguments.
Returns:
Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, float, Dict[str, Any]]:
1. New population.
2. New population fitness/function values.
3. New global best solution.
4. New global best solutions fitness/objective value.
5. Additional arguments:
* velocities (numpy.ndarray): velocities of cats.
"""
modes = self.random_seek_trace()
velocities = params.pop('velocities')
pop_copies = population.copy()
for k in range(len(pop_copies)):
if modes[k] == 0:
pop_copies[k], population_fitness[k], pop_copies, population_fitness = self.seeking_mode(task,
pop_copies[k],
population_fitness[k],
pop_copies,
population_fitness,
best_fitness)
else: # if cat in tracing mode
pop_copies[k], population_fitness[k], velocities[k] = self.tracing_mode(task,
pop_copies[k],
velocities[k],
best_x)
best_index = np.argmin(population_fitness)
if population_fitness[best_index] < best_fitness:
best_x, best_fitness = pop_copies[best_index].copy(), population_fitness[best_index]
return pop_copies, population_fitness, best_x, best_fitness, {'velocities': velocities}
