# !/usr/bin/env python
# Created by "Thieu" at 18:14, 10/04/2020 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import numpy as np
from mealpy.optimizer import Optimizer
[docs]class BaseES(Optimizer):
"""
The original version of: Evolution Strategies (ES)
Links:
1. http://www.cleveralgorithms.com/nature-inspired/evolution/evolution_strategies.html
Hyper-parameters should fine tuned in approximate range to get faster convergen toward the global optimum:
+ n_child (float/int): Number of child evolving in the next generation
+ if float number --> percentage of child agents, [0.5, 1.0]
+ int --> number of child agents, [20, pop_size]
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy.evolutionary_based.ES import BaseES
>>>
>>> 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
>>> n_child = 0.75
>>> model = BaseES(problem_dict1, epoch, pop_size, n_child)
>>> best_position, best_fitness = model.solve()
>>> print(f"Solution: {best_position}, Fitness: {best_fitness}")
References
~~~~~~~~~~
[1] Beyer, H.G. and Schwefel, H.P., 2002. Evolution strategies–a comprehensive introduction. Natural computing, 1(1), pp.3-52.
"""
ID_POS = 0
ID_TAR = 1
ID_STR = 2 # strategy
def __init__(self, problem, epoch=10000, pop_size=100, n_child=0.75, **kwargs):
"""
Args:
problem (dict): The problem dictionary
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size (miu in the paper), default = 100
n_child (float/int): if float number --> percentage of child agents, int --> number of child agents
"""
super().__init__(problem, kwargs)
self.epoch = epoch
self.pop_size = pop_size
if n_child < 1: # lamda, 75% of pop_size
self.n_child = int(n_child * self.pop_size)
else:
self.n_child = int(n_child)
self.distance = 0.05 * (self.problem.ub - self.problem.lb)
self.nfe_per_epoch = self.n_child
self.sort_flag = True
[docs] def create_solution(self):
"""
To get the position, fitness wrapper, target and obj list
+ A[self.ID_POS] --> Return: position
+ A[self.ID_TAR] --> Return: [target, [obj1, obj2, ...]]
+ A[self.ID_TAR][self.ID_FIT] --> Return: target
+ A[self.ID_TAR][self.ID_OBJ] --> Return: [obj1, obj2, ...]
Returns:
list: wrapper of solution with format [position, [target, [obj1, obj2, ...]], strategy]
"""
position = np.random.uniform(self.problem.lb, self.problem.ub)
position = self.amend_position(position)
fitness = self.get_fitness_position(position)
strategy = np.random.uniform(0, self.distance)
return [position, fitness, strategy]
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
child = []
for idx in range(0, self.n_child):
pos_new = self.pop[idx][self.ID_POS] + self.pop[idx][self.ID_STR] * np.random.normal(0, 1.0, self.problem.n_dims)
pos_new = self.amend_position(pos_new)
tau = np.sqrt(2.0 * self.problem.n_dims) ** -1.0
tau_p = np.sqrt(2.0 * np.sqrt(self.problem.n_dims)) ** -1.0
strategy = np.exp(tau_p * np.random.normal(0, 1.0, self.problem.n_dims) + tau * np.random.normal(0, 1.0, self.problem.n_dims))
child.append([pos_new, None, strategy])
child = self.update_fitness_population(child)
self.pop = self.get_sorted_strim_population(child + self.pop, self.pop_size)
[docs]class LevyES(BaseES):
"""
My Levy-flight version of: Evolution Strategies (ES)
Links:
1. http://www.cleveralgorithms.com/nature-inspired/evolution/evolution_strategies.html
Notes
~~~~~
I implement Levy-flight and change the flow of original version.
Hyper-parameters should fine tuned in approximate range to get faster convergen toward the global optimum:
+ n_child (float/int): Number of child evolving in the next generation
+ if float number --> percentage of child agents, [0.5, 1.0]
+ int --> number of child agents, [20, pop_size]
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy.evolutionary_based.ES import BaseES
>>>
>>> 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
>>> n_child = 0.75
>>> model = BaseES(problem_dict1, epoch, pop_size, n_child)
>>> best_position, best_fitness = model.solve()
>>> print(f"Solution: {best_position}, Fitness: {best_fitness}")
References
~~~~~~~~~~
[1] Beyer, H.G. and Schwefel, H.P., 2002. Evolution strategies–a comprehensive introduction. Natural computing, 1(1), pp.3-52.
"""
def __init__(self, problem, epoch=10000, pop_size=100, n_child=0.75, **kwargs):
"""
Args:
problem (dict): The problem dictionary
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size (miu in the paper), default = 100
n_child (float/int): if float number --> percentage of child agents, int --> number of child agents
"""
super().__init__(problem, epoch, pop_size, n_child, **kwargs)
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
self.nfe_per_epoch = 2 * self.n_child
child = []
for idx in range(0, self.n_child):
pos_new = self.pop[idx][self.ID_POS] + self.pop[idx][self.ID_STR] * np.random.normal(0, 1.0, self.problem.n_dims)
pos_new = self.amend_position(pos_new)
tau = np.sqrt(2.0 * self.problem.n_dims) ** -1.0
tau_p = np.sqrt(2.0 * np.sqrt(self.problem.n_dims)) ** -1.0
strategy = np.exp(tau_p * np.random.normal(0, 1.0, self.problem.n_dims) + tau * np.random.normal(0, 1.0, self.problem.n_dims))
child.append([pos_new, None, strategy])
child = self.update_fitness_population(child)
child_levy = []
for idx in range(0, self.n_child):
levy = self.get_levy_flight_step(multiplier=0.01, case=-1)
pos_new = self.pop[idx][self.ID_POS] + np.random.uniform(self.problem.lb, self.problem.ub) * \
levy * (self.pop[idx][self.ID_POS] - self.g_best[self.ID_POS])
pos_new = self.amend_position(pos_new)
tau = np.sqrt(2.0 * self.problem.n_dims) ** -1.0
tau_p = np.sqrt(2.0 * np.sqrt(self.problem.n_dims)) ** -1.0
stdevs = np.array([np.exp(tau_p * np.random.normal(0, 1.0) + tau * np.random.normal(0, 1.0)) for _ in range(self.problem.n_dims)])
child_levy.append([pos_new, None, stdevs])
child_levy = self.update_fitness_population(child_levy)
self.pop = self.get_sorted_strim_population(child + child_levy + self.pop, self.pop_size)