# !/usr/bin/env python
# Created by "Thieu" at 11:59, 17/03/2020 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import numpy as np
from copy import deepcopy
from mealpy.optimizer import Optimizer
[docs]class BaseMFO(Optimizer):
"""
My changed version of: Moth-Flame Optimization (MFO)
Notes
~~~~~
+ The flow of algorithm is changed
+ The old solution is updated
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy.swarm_based.MFO import BaseMFO
>>>
>>> def fitness_function(solution):
>>> return np.sum(solution**2)
>>>
>>> problem_dict1 = {
>>> "fit_func": fitness_function,
>>> "lb": [-10, -15, -4, -2, -8],
>>> "ub": [10, 15, 12, 8, 20],
>>> "minmax": "min",
>>> "verbose": True,
>>> }
>>>
>>> epoch = 1000
>>> pop_size = 50
>>> model = BaseMFO(problem_dict1, epoch, pop_size)
>>> best_position, best_fitness = model.solve()
>>> print(f"Solution: {best_position}, Fitness: {best_fitness}")
References
~~~~~~~~~~
[1] Mirjalili, S., 2015. Moth-flame optimization algorithm: A novel nature-inspired
heuristic paradigm. Knowledge-based systems, 89, pp.228-249.
"""
def __init__(self, problem, epoch=10000, pop_size=100, **kwargs):
"""
Args:
problem (dict): The problem dictionary
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size, default = 100
"""
super().__init__(problem, kwargs)
self.nfe_per_epoch = pop_size
self.sort_flag = False
self.epoch = epoch
self.pop_size = pop_size
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
# Number of flames Eq.(3.14) in the paper (linearly decreased)
num_flame = round(self.pop_size - (epoch + 1) * ((self.pop_size - 1) / self.epoch))
# a linearly decreases from -1 to -2 to calculate t in Eq. (3.12)
a = -1 + (epoch + 1) * ((-1) / self.epoch)
pop_flames, g_best = self.get_global_best_solution(self.pop)
pop_new = []
for idx in range(0, self.pop_size):
# D in Eq.(3.13)
distance_to_flame = np.abs(pop_flames[idx][self.ID_POS] - self.pop[idx][self.ID_POS])
t = (a - 1) * np.random.uniform(0, 1, self.problem.n_dims) + 1
b = 1
# Update the position of the moth with respect to its corresponding flame, Eq.(3.12).
temp_1 = distance_to_flame * np.exp(b * t) * np.cos(t * 2 * np.pi) + pop_flames[idx][self.ID_POS]
# Update the position of the moth with respect to one flame Eq.(3.12).
## Here is a changed, I used the best position of flames not the position num_flame th (as original code)
temp_2 = distance_to_flame * np.exp(b * t) * np.cos(t * 2 * np.pi) + g_best[self.ID_POS]
list_idx = idx * np.ones(self.problem.n_dims)
pos_new = np.where(list_idx < num_flame, temp_1, temp_2)
## This is the way I make this algorithm working. I tried to run matlab code with large dimension and it doesn't convergence.
pos_new = self.amend_position(pos_new)
pop_new.append([pos_new, None])
pop_new = self.update_fitness_population(pop_new)
self.pop = self.greedy_selection_population(self.pop, pop_new)
[docs]class OriginalMFO(BaseMFO):
"""
The original version of: Moth-flame Optimization (MFO)
Link:
1. https://www.mathworks.com/matlabcentral/fileexchange/52269-moth-flame-optimization-mfo-algorithm
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy.swarm_based.MFO import OriginalMFO
>>>
>>> def fitness_function(solution):
>>> return np.sum(solution**2)
>>>
>>> problem_dict1 = {
>>> "fit_func": fitness_function,
>>> "lb": [-10, -15, -4, -2, -8],
>>> "ub": [10, 15, 12, 8, 20],
>>> "minmax": "min",
>>> "verbose": True,
>>> }
>>>
>>> epoch = 1000
>>> pop_size = 50
>>> model = OriginalMFO(problem_dict1, epoch, pop_size)
>>> best_position, best_fitness = model.solve()
>>> print(f"Solution: {best_position}, Fitness: {best_fitness}")
References
~~~~~~~~~~
[1] Mirjalili, S., 2015. Moth-flame optimization algorithm: A novel nature-inspired
heuristic paradigm. Knowledge-based systems, 89, pp.228-249.
"""
def __init__(self, problem, epoch=10000, pop_size=100, **kwargs):
"""
Args:
problem (dict): The problem dictionary
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size, default = 100
"""
super().__init__(problem, epoch, pop_size, **kwargs)
self.nfe_per_epoch = pop_size
self.sort_flag = False
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
# Number of flames Eq.(3.14) in the paper (linearly decreased)
num_flame = round(self.pop_size - (epoch + 1) * ((self.pop_size - 1) / self.epoch))
# a linearly decreases from -1 to -2 to calculate t in Eq. (3.12)
a = -1 + (epoch + 1) * ((-1) / self.epoch)
pop_flames, g_best = self.get_global_best_solution(self.pop)
pop_new = []
for idx in range(0, self.pop_size):
pos_new = deepcopy(self.pop[idx][self.ID_POS])
for j in range(self.problem.n_dims):
# D in Eq.(3.13)
distance_to_flame = np.abs(pop_flames[idx][self.ID_POS][j] - self.pop[idx][self.ID_POS][j])
t = (a - 1) * np.random.uniform() + 1
b = 1
if idx <= num_flame: # Update the position of the moth with respect to its corresponding flame
# Eq.(3.12)
pos_new[j] = distance_to_flame * np.exp(b * t) * np.cos(t * 2 * np.pi) + pop_flames[idx][self.ID_POS][j]
else: # Update the position of the moth with respect to one flame
# Eq.(3.12).
## Here is a changed, I used the best position of flames not the position num_flame th (as original code)
pos_new[j] = distance_to_flame * np.exp(b * t) * np.cos(t * 2 * np.pi) + pop_flames[num_flame][self.ID_POS][j]
pos_new = self.amend_position(pos_new)
pop_new.append([pos_new, None])
self.pop = self.update_fitness_population(pop_new)