# encoding=utf8
import logging
from numpy import apply_along_axis, zeros, argsort, concatenate, array, exp, cos, pi
from NiaPy.algorithms.algorithm import Algorithm
logging.basicConfig()
logger = logging.getLogger('NiaPy.algorithms.basic')
logger.setLevel('INFO')
__all__ = ['MothFlameOptimizer']
[docs]class MothFlameOptimizer(Algorithm):
r"""MothFlameOptimizer of Moth flame optimizer.
Algorithm:
Moth flame optimizer
Date:
2018
Author:
Kivanc Guckiran and Klemen Berkovič
License:
MIT
Reference paper:
Mirjalili, Seyedali. "Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm." Knowledge-Based Systems 89 (2015): 228-249.
Attributes:
Name (List[str]): List of strings representing algorithm name.
See Also:
* :class:`NiaPy.algorithms.algorithm.Algorithm`
"""
Name = ['MothFlameOptimizer', 'MFO']
[docs] @staticmethod
def algorithmInfo():
r"""Get basic information of algorithm.
Returns:
str: Basic information.
See Also:
* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
"""
return r"""Mirjalili, Seyedali. "Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm." Knowledge-Based Systems 89 (2015): 228-249."""
[docs] @staticmethod
def typeParameters():
r"""Get dictionary with functions for checking values of parameters.
Returns:
Dict[str, Callable]: TODO
See Also:
* :func:`NiaPy.algorithms.algorithm.Algorithm.typeParameters`
"""
return Algorithm.typeParameters()
[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.Algorithm.setParameters`
"""
Algorithm.setParameters(self, NP=NP, **ukwargs)
[docs] def initPopulation(self, task):
r"""Initialize starting population.
Args:
task (Task): Optimization task
Returns:
Tuple[numpy.ndarray, numpy.ndarray[float], Dict[str, Any]]:
1. Initialized population
2. Initialized population function/fitness values
3. Additional arguments:
* best_flames (numpy.ndarray): Best individuals
* best_flame_fitness (numpy.ndarray): Best individuals fitness/function values
* previous_population (numpy.ndarray): Previous population
* previous_fitness (numpy.ndarray[float]): Previous population fitness/function values
See Also:
* :func:`NiaPy.algorithms.algorithm.Algorithm.initPopulation`
"""
moth_pos, moth_fitness, d = Algorithm.initPopulation(self, task)
# Create best population
indexes = argsort(moth_fitness)
best_flames, best_flame_fitness = moth_pos[indexes], moth_fitness[indexes]
# Init previous population
previous_population, previous_fitness = zeros((self.NP, task.D)), zeros(self.NP)
d.update({'best_flames': best_flames, 'best_flame_fitness': best_flame_fitness, 'previous_population': previous_population, 'previous_fitness': previous_fitness})
return moth_pos, moth_fitness, d
[docs] def runIteration(self, task, moth_pos, moth_fitness, xb, fxb, best_flames, best_flame_fitness, previous_population, previous_fitness, **dparams):
r"""Core function of MothFlameOptimizer algorithm.
Args:
task (Task): Optimization task.
moth_pos (numpy.ndarray): Current population.
moth_fitness (numpy.ndarray): Current population fitness/function values.
xb (numpy.ndarray): Current population best individual.
fxb (float): Current best individual.
best_flames (numpy.ndarray): Best found individuals.
best_flame_fitness (numpy.ndarray): Best found individuals fitness/function values.
previous_population (numpy.ndarray): Previous population.
previous_fitness (numpy.ndarray): Previous population fitness/function values.
**dparams (Dict[str, Any]): Additional parameters
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 fitness/objective value.
5. Additional arguments:
* best_flames (numpy.ndarray): Best individuals.
* best_flame_fitness (numpy.ndarray): Best individuals fitness/function values.
* previous_population (numpy.ndarray): Previous population.
* previous_fitness (numpy.ndarray): Previous population fitness/function values.
"""
# Previous positions
previous_population, previous_fitness = moth_pos, moth_fitness
# Create sorted population
indexes = argsort(moth_fitness)
sorted_population = moth_pos[indexes]
# Some parameters
flame_no, a = round(self.NP - task.Iters * ((self.NP - 1) / task.nGEN)), -1 + task.Iters * ((-1) / task.nGEN)
for i in range(self.NP):
for j in range(task.D):
distance_to_flame, b, t = abs(sorted_population[i, j] - moth_pos[i, j]), 1, (a - 1) * self.rand() + 1
if i <= flame_no: moth_pos[i, j] = distance_to_flame * exp(b * t) * cos(2 * pi * t) + sorted_population[i, j]
else: moth_pos[i, j] = distance_to_flame * exp(b * t) * cos(2 * pi * t) + sorted_population[flame_no, j]
moth_pos = apply_along_axis(task.repair, 1, moth_pos, self.Rand)
moth_fitness = apply_along_axis(task.eval, 1, moth_pos)
xb, fxb = self.getBest(moth_pos, moth_fitness, xb, fxb)
double_population, double_fitness = concatenate((previous_population, best_flames), axis=0), concatenate((previous_fitness, best_flame_fitness), axis=0)
indexes = argsort(double_fitness)
double_sorted_fitness, double_sorted_population = double_fitness[indexes], double_population[indexes]
for newIdx in range(2 * self.NP): double_sorted_population[newIdx] = array(double_population[indexes[newIdx], :])
best_flame_fitness, best_flames = double_sorted_fitness[:self.NP], double_sorted_population[:self.NP]
return moth_pos, moth_fitness, xb, fxb, {'best_flames': best_flames, 'best_flame_fitness': best_flame_fitness, 'previous_population': previous_population, 'previous_fitness': previous_fitness}
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