# !/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 mealpy.optimizer import Optimizer
[docs]class BaseGWO(Optimizer):
"""
The original version of: Grey Wolf Optimizer (GWO)
Links:
1. https://doi.org/10.1016/j.advengsoft.2013.12.007
2. https://www.mathworks.com/matlabcentral/fileexchange/44974-grey-wolf-optimizer-gwo?s_tid=FX_rc3_behav
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy.swarm_based.GWO import BaseGWO
>>>
>>> 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 = BaseGWO(problem_dict1, epoch, pop_size)
>>> best_position, best_fitness = model.solve()
>>> print(f"Solution: {best_position}, Fitness: {best_fitness}")
References
~~~~~~~~~~
[1] Mirjalili, S., Mirjalili, S.M. and Lewis, A., 2014. Grey wolf optimizer. Advances in engineering software, 69, pp.46-61.
"""
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
"""
# linearly decreased from 2 to 0
a = 2 - 2 * epoch / (self.epoch - 1)
_, list_best, _ = self.get_special_solutions(self.pop, best=3)
pop_new = []
for idx in range(0, self.pop_size):
A1, A2, A3 = a * (2 * np.random.uniform() - 1), a * (2 * np.random.uniform() - 1), a * (2 * np.random.uniform() - 1)
C1, C2, C3 = 2 * np.random.uniform(), 2 * np.random.uniform(), 2 * np.random.uniform()
X1 = list_best[0][self.ID_POS] - A1 * np.abs(C1 * list_best[0][self.ID_POS] - self.pop[idx][self.ID_POS])
X2 = list_best[1][self.ID_POS] - A2 * np.abs(C2 * list_best[1][self.ID_POS] - self.pop[idx][self.ID_POS])
X3 = list_best[2][self.ID_POS] - A3 * np.abs(C3 * list_best[2][self.ID_POS] - self.pop[idx][self.ID_POS])
pos_new = (X1 + X2 + X3) / 3.0
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 RW_GWO(Optimizer):
"""
The original version of: Random Walk Grey Wolf Optimizer (RW-GWO)
Notes
~~~~~
+ This version is always performs worst than BaseGWO. Not sure why paper is accepted at Swarm and evolutionary computation
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy.swarm_based.GWO import RW_GWO
>>>
>>> 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 = RW_GWO(problem_dict1, epoch, pop_size)
>>> best_position, best_fitness = model.solve()
>>> print(f"Solution: {best_position}, Fitness: {best_fitness}")
References
~~~~~~~~~~
[1] Gupta, S. and Deep, K., 2019. A novel random walk grey wolf optimizer. Swarm and evolutionary computation, 44, pp.101-112.
"""
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 + 3
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
"""
# linearly decreased from 2 to 0, Eq. 5
b = 2 - 2 * epoch / (self.epoch - 1)
# linearly decreased from 2 to 0
a = 2 - 2 * epoch / (self.epoch - 1)
_, leaders, _ = self.get_special_solutions(self.pop, best=3)
## Random walk here
leaders_new = []
for i in range(0, len(leaders)):
pos_new = leaders[i][self.ID_POS] + a * np.random.standard_cauchy(self.problem.n_dims)
pos_new = self.amend_position(pos_new)
leaders_new.append([pos_new, None])
leaders_new = self.update_fitness_population(leaders_new)
leaders = self.greedy_selection_population(leaders, leaders_new)
## Update other wolfs
pop_new = []
for idx in range(0, self.pop_size):
# Eq. 3
miu1, miu2, miu3 = b * (2 * np.random.uniform() - 1), b * (2 * np.random.uniform() - 1), b * (2 * np.random.uniform() - 1)
# Eq. 4
c1, c2, c3 = 2 * np.random.uniform(), 2 * np.random.uniform(), 2 * np.random.uniform()
X1 = leaders[0][self.ID_POS] - miu1 * np.abs(c1 * self.g_best[self.ID_POS] - self.pop[idx][self.ID_POS])
X2 = leaders[1][self.ID_POS] - miu2 * np.abs(c2 * self.g_best[self.ID_POS] - self.pop[idx][self.ID_POS])
X3 = leaders[2][self.ID_POS] - miu3 * np.abs(c3 * self.g_best[self.ID_POS] - self.pop[idx][self.ID_POS])
pos_new = (X1 + X2 + X3) / 3.0
pos_new = self.amend_position(pos_new)
pop_new.append([pos_new, None])
pop_new = self.update_fitness_population(pop_new)
pop_new = self.greedy_selection_population(self.pop, pop_new)
self.pop = self.get_sorted_strim_population(pop_new + leaders, self.pop_size)