# !/usr/bin/env python
# Created by "Thieu" at 10:06, 17/03/2020 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import numpy as np
from mealpy.optimizer import Optimizer
[docs]class BaseWOA(Optimizer):
"""
The original version of: Whale Optimization Algorithm (WOA)
Links:
1. https://doi.org/10.1016/j.advengsoft.2016.01.008
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy.swarm_based.WOA import BaseWOA
>>>
>>> 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 = BaseWOA(problem_dict1, epoch, pop_size)
>>> best_position, best_fitness = model.solve()
>>> print(f"Solution: {best_position}, Fitness: {best_fitness}")
References
~~~~~~~~~~
[1] Mirjalili, S. and Lewis, A., 2016. The whale optimization algorithm.
Advances in engineering software, 95, pp.51-67.
"""
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
"""
a = 2 - 2 * epoch / (self.epoch - 1) # linearly decreased from 2 to 0
pop_new = []
for idx in range(0, self.pop_size):
r = np.random.rand()
A = 2 * a * r - a
C = 2 * r
l = np.random.uniform(-1, 1)
p = 0.5
b = 1
if np.random.uniform() < p:
if np.abs(A) < 1:
D = np.abs(C * self.g_best[self.ID_POS] - self.pop[idx][self.ID_POS])
pos_new = self.g_best[self.ID_POS] - A * D
else:
# x_rand = pop[np.random.np.random.randint(self.pop_size)] # select random 1 position in pop
x_rand = self.create_solution()
D = np.abs(C * x_rand[self.ID_POS] - self.pop[idx][self.ID_POS])
pos_new = x_rand[self.ID_POS] - A * D
else:
D1 = np.abs(self.g_best[self.ID_POS] - self.pop[idx][self.ID_POS])
pos_new = self.g_best[self.ID_POS] + np.exp(b * l) * np.cos(2 * np.pi * l) * D1
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 HI_WOA(Optimizer):
"""
The original version of: Hybrid Improved Whale Optimization Algorithm (HI-WOA)
Links:
1. https://ieenp.explore.ieee.org/document/8900003
Hyper-parameters should fine tuned in approximate range to get faster convergen toward the global optimum:
+ feedback_max (int): maximum iterations of each feedback, default = 10
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy.swarm_based.WOA import HI_WOA
>>>
>>> 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
>>> feedback_max = 10
>>> model = HI_WOA(problem_dict1, epoch, pop_size, feedback_max)
>>> best_position, best_fitness = model.solve()
>>> print(f"Solution: {best_position}, Fitness: {best_fitness}")
References
~~~~~~~~~~
[1] Tang, C., Sun, W., Wu, W. and Xue, M., 2019, July. A hybrid improved whale optimization algorithm.
In 2019 IEEE 15th International Conference on Control and Automation (ICCA) (pp. 362-367). IEEE.
"""
def __init__(self, problem, epoch=10000, pop_size=100, feedback_max=10, **kwargs):
"""
Args:
problem (dict): The problem dictionary
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size, default = 100
feedback_max (int): maximum iterations of each feedback, default = 10
"""
super().__init__(problem, kwargs)
self.nfe_per_epoch = pop_size
self.sort_flag = True
self.epoch = epoch
self.pop_size = pop_size
self.feedback_max = feedback_max
# The maximum of times g_best doesn't change -> need to change half of population
self.n_changes = int(pop_size / 2)
## Dynamic variable
self.dyn_feedback_count = 0
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
nfe_epoch = 0
a = 2 + 2 * np.cos(np.pi / 2 * (1 + epoch / self.epoch)) # Eq. 8
pop_new = []
for idx in range(0, self.pop_size):
r = np.random.rand()
A = 2 * a * r - a
C = 2 * r
l = np.random.uniform(-1, 1)
p = 0.5
b = 1
if np.random.uniform() < p:
if np.abs(A) < 1:
D = np.abs(C * self.g_best[self.ID_POS] - self.pop[idx][self.ID_POS])
pos_new = self.g_best[self.ID_POS] - A * D
else:
# x_rand = pop[np.random.np.random.randint(self.pop_size)] # select random 1 position in pop
x_rand = self.create_solution()
D = np.abs(C * x_rand[self.ID_POS] - self.pop[idx][self.ID_POS])
pos_new = x_rand[self.ID_POS] - A * D
else:
D1 = np.abs(self.g_best[self.ID_POS] - self.pop[idx][self.ID_POS])
pos_new = self.g_best[self.ID_POS] + np.exp(b * l) * np.cos(2 * np.pi * l) * D1
pos_new = self.amend_position(pos_new)
pop_new.append([pos_new, None])
pop_new = self.update_fitness_population(pop_new)
nfe_epoch += self.pop_size
## Feedback Mechanism
_, current_best = self.get_global_best_solution(pop_new)
if current_best[self.ID_TAR][self.ID_FIT] == self.g_best[self.ID_TAR][self.ID_FIT]:
self.dyn_feedback_count += 1
else:
self.dyn_feedback_count = 0
if self.dyn_feedback_count >= self.feedback_max:
idx_list = np.random.choice(range(0, self.pop_size), self.n_changes, replace=False)
pop_child = self.create_population(self.n_changes)
nfe_epoch += self.n_changes
for idx_counter, idx in enumerate(idx_list):
pop_new[idx] = pop_child[idx_counter]
self.pop = pop_new
self.nfe_per_epoch = nfe_epoch