# encoding=utf8
import logging
from numpy import random as rand, sin, pi, argmin, abs, mean
from scipy.special import gamma
from NiaPy.algorithms.algorithm import Algorithm
logging.basicConfig()
logger = logging.getLogger('NiaPy.algorithms.basic')
logger.setLevel('INFO')
__all__ = ['HarrisHawksOptimization']
[docs]class HarrisHawksOptimization(Algorithm):
r"""Implementation of Harris Hawks Optimization algorithm.
Algorithm:
Harris Hawks Optimization
Date:
2020
Authors:
Francisco Jose Solis-Munoz
License:
MIT
Reference paper:
Heidari et al. "Harris hawks optimization: Algorithm and applications". Future Generation Computer Systems. 2019. Vol. 97. 849-872.
Attributes:
Name (List[str]): List of strings representing algorithm name.
levy (float): Levy factor.
See Also:
* :class:`NiaPy.algorithms.Algorithm`
"""
Name = ['HarrisHawksOptimization', 'HHO']
def __init__(self, **kwargs):
super(HarrisHawksOptimization, self).__init__(**kwargs)
[docs] @staticmethod
def algorithmInfo():
r"""Get algorithms information.
Returns:
str: Algorithm information.
See Also:
* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
"""
return r"""Heidari et al. "Harris hawks optimization: Algorithm and applications". Future Generation Computer Systems. 2019. Vol. 97. 849-872."""
[docs] @staticmethod
def typeParameters():
r"""Return dict with where key of dict represents parameter name and values represent checking functions for selected parameter.
Returns:
Dict[str, Callable]:
* levy (Callable[[Union[float, int]], bool]): Levy factor.
See Also:
* :func:`NiaPy.algorithms.Algorithm.typeParameters`
"""
d = Algorithm.typeParameters()
d.update({
'levy': lambda x: isinstance(x, (float, int)) and x > 0,
})
return d
[docs] def setParameters(self, NP=40, levy=0.01, **ukwargs):
r"""Set the parameters of the algorithm.
Args:
levy (Optional[float]): Levy factor.
See Also:
* :func:`NiaPy.algorithms.Algorithm.setParameters`
"""
Algorithm.setParameters(self, NP=NP, **ukwargs)
self.levy = levy
[docs] def getParameters(self):
r"""Get parameters of the algorithm.
Returns:
Dict[str, Any]
"""
d = Algorithm.getParameters(self)
d.update({
'levy': self.levy
})
return d
[docs] def initPopulation(self, task, rnd=rand):
r"""Initialize the starting population.
Parameters:
task (Task): Optimization task
Returns:
Tuple[numpy.ndarray, numpy.ndarray[float], Dict[str, Any]]:
1. New population.
2. New population fitness/function values.
See Also:
* :func:`NiaPy.algorithms.Algorithm.initPopulation`
"""
Sol, Fitness, d = Algorithm.initPopulation(self, task)
return Sol, Fitness, d
[docs] def levy_function(self, dims, step=0.01, rnd=rand):
r"""Calculate levy function.
Parameters:
dim (int): Number of dimensions
step (float): Step of the Levy function
Returns:
float: The Levy function evaluation
"""
beta = 1.5
sigma = (gamma(1 + beta) * sin(pi * beta / 2) / (gamma((1 + beta / 2) * beta * 2.0 ** ((beta - 1) / 2)))) ** (1 / beta)
normal_1 = rnd.normal(0, sigma, size=dims)
normal_2 = rnd.normal(0, 1, size=dims)
result = step * normal_1 / (abs(normal_2) ** (1 / beta))
return result
[docs] def runIteration(self, task, Sol, Fitness, xb, fxb, **dparams):
r"""Core function of Harris Hawks Optimization.
Parameters:
task (Task): Optimization task.
Sol (numpy.ndarray): Current population
Fitness (numpy.ndarray[float]): Current population fitness/funciton values
xb (numpy.ndarray): Current best individual
fxb (float): Current best individual function/fitness value
dparams (Dict[str, Any]): Additional algorithm arguments
Returns:
Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, float, Dict[str, Any]]:
1. New population
2. New population fitness/function vlues
3. New global best solution
4. New global best fitness/objective value
"""
# Decreasing energy factor
decreasing_energy_factor = 2 * (1 - task.iters() / task.nGEN)
mean_sol = mean(Sol)
# Update population
for i in range(self.NP):
jumping_energy = self.Rand.uniform(0, 2)
decreasing_energy_random = self.Rand.uniform(-1, 1)
escaping_energy = decreasing_energy_factor * decreasing_energy_random
escaping_energy_abs = abs(escaping_energy)
random_number = self.Rand.rand()
if escaping_energy >= 1 and random_number >= 0.5:
# 0. Exploration: Random tall tree
rhi = self.Rand.randint(0, self.NP)
random_agent = Sol[rhi]
Sol[i] = random_agent - self.Rand.rand() * abs(random_agent - 2 * self.Rand.rand() * Sol[i])
elif escaping_energy_abs >= 1 and random_number < 0.5:
# 1. Exploration: Family members mean
Sol[i] = (xb - mean_sol) - self.Rand.rand() * self.Rand.uniform(task.Lower, task.Upper)
elif escaping_energy_abs >= 0.5 and random_number >= 0.5:
# 2. Exploitation: Soft besiege
Sol[i] = \
(xb - Sol[i]) - \
escaping_energy * \
abs(jumping_energy * xb - Sol[i])
elif escaping_energy_abs < 0.5 and random_number >= 0.5:
# 3. Exploitation: Hard besiege
Sol[i] = \
xb - \
escaping_energy * \
abs(xb - Sol[i])
elif escaping_energy_abs >= 0.5 and random_number < 0.5:
# 4. Exploitation: Soft besiege with pprogressive rapid dives
cand1 = task.repair(xb - escaping_energy * abs(jumping_energy * xb - Sol[i]), rnd=self.Rand)
random_vector = self.Rand.rand(task.D)
cand2 = task.repair(cand1 + random_vector * self.levy_function(task.D, self.levy, rnd=self.Rand), rnd=self.Rand)
if task.eval(cand1) < Fitness[i]:
Sol[i] = cand1
elif task.eval(cand2) < Fitness[i]:
Sol[i] = cand2
elif escaping_energy_abs < 0.5 and random_number < 0.5:
# 5. Exploitation: Hard besiege with progressive rapid dives
cand1 = task.repair(xb - escaping_energy * abs(jumping_energy * xb - mean_sol), rnd=self.Rand)
random_vector = self.Rand.rand(task.D)
cand2 = task.repair(cand1 + random_vector * self.levy_function(task.D, self.levy, rnd=self.Rand), rnd=self.Rand)
if task.eval(cand1) < Fitness[i]:
Sol[i] = cand1
elif task.eval(cand2) < Fitness[i]:
Sol[i] = cand2
# Repair agent (from population) values
Sol[i] = task.repair(Sol[i], rnd=self.Rand)
# Eval population
Fitness[i] = task.eval(Sol[i])
# Get best of population
best_index = argmin(Fitness)
xb_cand = Sol[best_index].copy()
fxb_cand = Fitness[best_index].copy()
if fxb_cand < fxb:
fxb = fxb_cand
xb = xb_cand.copy()
return Sol, Fitness, xb, fxb, {}
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