# encoding=utf8
import numpy as np
from NiaPy.algorithms.algorithm import Algorithm
__all__ = ['GreyWolfOptimizer']
[docs]class GreyWolfOptimizer(Algorithm):
r"""Implementation of Grey wolf optimizer.
Algorithm:
Grey wolf optimizer
Date:
2018
Author:
Iztok Fister Jr. and Klemen Berkovič
License:
MIT
Reference paper:
* Mirjalili, Seyedali, Seyed Mohammad Mirjalili, and Andrew Lewis. "Grey wolf optimizer." Advances in engineering software 69 (2014): 46-61.
* Grey Wolf Optimizer (GWO) source code version 1.0 (MATLAB) from MathWorks
Attributes:
Name (List[str]): List of strings representing algorithm names.
See Also:
* :class:`NiaPy.algorithms.Algorithm`
"""
Name = ['GreyWolfOptimizer', 'GWO']
[docs] @staticmethod
def algorithmInfo():
r"""Get algorithm information.
Returns:
str: Algorithm information.
See Also:
* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
"""
return r"""Mirjalili, Seyedali, Seyed Mohammad Mirjalili, and Andrew Lewis. "Grey wolf optimizer." Advances in engineering software 69 (2014): 46-61."""
[docs] @staticmethod
def typeParameters(): return {
'NP': lambda x: isinstance(x, int) and x > 0
}
[docs] def setParameters(self, NP=25, **ukwargs):
r"""Set the algorithm parameters.
Arguments:
NP (int): Number of individuals in population
See Also:
* :func:`NiaPy.algorithms.Algorithm.setParameters`
"""
Algorithm.setParameters(self, NP=NP, **ukwargs)
[docs] def initPopulation(self, task):
r"""Initialize population.
Args:
task (Task): Optimization task.
Returns:
Tuple[numpy.ndarray, numpy.ndarray, Dict[str, Any]]:
1. Initialized population.
2. Initialized populations fitness/function values.
3. Additional arguments:
* A (): TODO
See Also:
* :func:`NiaPy.algorithms.Algorithm.initPopulation`
"""
pop, fpop, d = Algorithm.initPopulation(self, task)
si = np.argsort(fpop)
A, A_f, B, B_f, D, D_f = np.copy(pop[si[0]]), fpop[si[0]], np.copy(pop[si[1]]), fpop[si[1]], np.copy(pop[si[2]]), fpop[si[2]]
d.update({'A': A, 'A_f': A_f, 'B': B, 'B_f': B_f, 'D': D, 'D_f': D_f})
return pop, fpop, d
[docs] def runIteration(self, task, pop, fpop, xb, fxb, A, A_f, B, B_f, D, D_f, **dparams):
r"""Core funciton of GreyWolfOptimizer algorithm.
Args:
task (Task): Optimization task.
pop (numpy.ndarray): Current population.
fpop (numpy.ndarray): Current populations function/fitness values.
xb (numpy.ndarray):
fxb (float):
A (numpy.ndarray):
A_f (float):
B (numpy.ndarray):
B_f (float):
D (numpy.ndarray):
D_f (float):
**dparams (Dict[str, Any]): Additional arguments.
Returns:
Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, float, Dict[str, Any]]:
1. New population
2. New population fitness/function values
3. Additional arguments:
* A (): TODO
"""
a = 2 - task.Evals * (2 / task.nFES)
for i, w in enumerate(pop):
A1, C1 = 2 * a * self.rand(task.D) - a, 2 * self.rand(task.D)
X1 = A - A1 * np.fabs(C1 * A - w)
A2, C2 = 2 * a * self.rand(task.D) - a, 2 * self.rand(task.D)
X2 = B - A2 * np.fabs(C2 * B - w)
A3, C3 = 2 * a * self.rand(task.D) - a, 2 * self.rand(task.D)
X3 = D - A3 * np.fabs(C3 * D - w)
pop[i] = task.repair((X1 + X2 + X3) / 3, self.Rand)
fpop[i] = task.eval(pop[i])
for i, f in enumerate(fpop):
if f < A_f: A, A_f = pop[i].copy(), f
elif A_f < f < B_f: B, B_f = pop[i].copy(), f
elif B_f < f < D_f: D, D_f = pop[i].copy(), f
xb, fxb = self.getBest(A, A_f, xb, fxb)
return pop, fpop, xb, fxb, {'A': A, 'A_f': A_f, 'B': B, 'B_f': B_f, 'D': D, 'D_f': D_f}
