# !/usr/bin/env python
# Created by "Thieu" at 14:52, 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 BaseNMRA(Optimizer):
"""
The original version of: Naked Mole-Rat Algorithm (NMRA)
Links:
1. https://www.doi.org10.1007/s00521-019-04464-7
Hyper-parameters should fine tuned in approximate range to get faster convergen toward the global optimum:
+ pb (float): [0.5, 0.95], probability of breeding, default = 0.75
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy.swarm_based.NMRA import BaseNMRA
>>>
>>> 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
>>> pb = 0.75
>>> model = BaseNMRA(problem_dict1, epoch, pop_size, pb)
>>> best_position, best_fitness = model.solve()
>>> print(f"Solution: {best_position}, Fitness: {best_fitness}")
References
~~~~~~~~~~
[1] Salgotra, R. and Singh, U., 2019. The naked mole-rat algorithm.
Neural Computing and Applications, 31(12), pp.8837-8857.
"""
def __init__(self, problem, epoch=10000, pop_size=100, pb=0.75, **kwargs):
"""
Args:
problem (dict): The problem dictionary
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size, default = 100
pb (float): probability of breeding, default = 0.75
"""
super().__init__(problem, kwargs)
self.nfe_per_epoch = self.pop_size
self.sort_flag = True
self.epoch = epoch
self.pop_size = pop_size
self.size_b = int(self.pop_size / 5)
self.pb = pb
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
pop_new = []
for idx in range(0, self.pop_size):
pos_new = deepcopy(self.pop[idx][self.ID_POS])
if idx < self.size_b: # breeding operators
if np.random.uniform() < self.pb:
alpha = np.random.uniform()
pos_new = (1 - alpha) * self.pop[idx][self.ID_POS] + alpha * (self.g_best[self.ID_POS] - self.pop[idx][self.ID_POS])
else: # working operators
t1, t2 = np.random.choice(range(self.size_b, self.pop_size), 2, replace=False)
pos_new = self.pop[idx][self.ID_POS] + np.random.uniform() * (self.pop[t1][self.ID_POS] - self.pop[t2][self.ID_POS])
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 ImprovedNMRA(Optimizer):
"""
The original version of: Naked Mole-Rat Algorithm (I-NMRA)
Notes:
+ Use mutation probability idea
+ Use crossover operator
+ Use Levy-flight technique
Hyper-parameters should fine tuned in approximate range to get faster convergen toward the global optimum:
+ pb (float): [0.5, 0.95], probability of breeding, default = 0.75
+ pm (float): [0.01, 0.1], probability of mutation, default = 0.01
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy.swarm_based.NMRA import ImprovedNMRA
>>>
>>> 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
>>> pb = 0.75
>>> pm = 0.01
>>> model = ImprovedNMRA(problem_dict1, epoch, pop_size, pb, pm)
>>> best_position, best_fitness = model.solve()
>>> print(f"Solution: {best_position}, Fitness: {best_fitness}")
References
~~~~~~~~~~
[1] Salgotra, R. and Singh, U., 2019. The naked mole-rat algorithm.
Neural Computing and Applications, 31(12), pp.8837-8857.
"""
def __init__(self, problem, epoch=10000, pop_size=100, pb=0.75, pm=0.01, **kwargs):
"""
Args:
problem (dict): The problem dictionary
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size, default = 100
pb (float): breeding probability, default = 0.75
pm (float): probability of mutation, default = 0.01
"""
super().__init__(problem, kwargs)
self.nfe_per_epoch = self.pop_size
self.sort_flag = True
self.epoch = epoch
self.pop_size = pop_size
self.size_b = int(self.pop_size / 5)
self.pb = pb
self.pm = pm
def _crossover_random(self, pop, g_best):
start_point = np.random.randint(0, self.problem.n_dims / 2)
id1 = start_point
id2 = int(start_point + self.problem.n_dims / 3)
id3 = int(self.problem.n_dims)
partner = pop[np.random.randint(0, self.pop_size)][self.ID_POS]
new_temp = deepcopy(g_best[self.ID_POS])
new_temp[0:id1] = g_best[self.ID_POS][0:id1]
new_temp[id1:id2] = partner[id1:id2]
new_temp[id2:id3] = g_best[self.ID_POS][id2:id3]
return new_temp
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
pop_new = []
for idx in range(0, self.pop_size):
# Exploration
if idx < self.size_b: # breeding operators
if np.random.uniform() < self.pb:
pos_new = self.pop[idx][self.ID_POS] + np.random.normal(0, 1, self.problem.n_dims) * \
(self.g_best[self.ID_POS] - self.pop[idx][self.ID_POS])
else:
levy_step = self.get_levy_flight_step(beta=1, multiplier=0.001, case=-1)
pos_new = self.pop[idx][self.ID_POS] + 1.0 / np.sqrt(epoch + 1) * np.sign(np.random.random() - 0.5) * \
levy_step * (self.pop[idx][self.ID_POS] - self.g_best[self.ID_POS])
# Exploitation
else: # working operators
if np.random.uniform() < 0.5:
t1, t2 = np.random.choice(range(self.size_b, self.pop_size), 2, replace=False)
pos_new = self.pop[idx][self.ID_POS] + np.random.normal(0, 1, self.problem.n_dims) * \
(self.pop[t1][self.ID_POS] - self.pop[t2][self.ID_POS])
else:
pos_new = self._crossover_random(self.pop, self.g_best)
# Mutation
temp = np.random.uniform(self.problem.lb, self.problem.ub)
pos_new = np.where(np.random.uniform(0, 1, self.problem.n_dims) < self.pm, temp, pos_new)
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)