#!/usr/bin/env python
# Created by "Thieu" at 20:22, 12/06/2020 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import numpy as np
from copy import deepcopy
from mealpy.optimizer import Optimizer
[docs]class BaseSMA(Optimizer):
"""
My changed version of: Slime Mould Algorithm (SMA)
Notes
~~~~~
+ Selected 2 unique and random solution to create new solution (not to create variable) --> remove third loop in original version
+ Check bound and update fitness after each individual move instead of after the whole population move in the original version
+ This version not only faster but also better than the original version
Hyper-parameters should fine tuned in approximate range to get faster convergen toward the global optimum:
+ pr (float): [0.01, 0.1], probability threshold (z in the paper)
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy.bio_based.SMA import BaseSMA
>>>
>>> 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
>>> pr = 0.03
>>> model = BaseSMA(problem_dict1, epoch, pop_size, pr)
>>> best_position, best_fitness = model.solve()
>>> print(f"Solution: {best_position}, Fitness: {best_fitness}")
"""
ID_WEI = 2
def __init__(self, problem, epoch=10000, pop_size=100, pr=0.03, **kwargs):
"""
Args:
problem (dict): The problem dictionary
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size, default = 100
pr (float): probability threshold (z in the paper), default = 0.03
"""
super().__init__(problem, kwargs)
self.nfe_per_epoch = pop_size
self.sort_flag = True
self.epoch = epoch
self.pop_size = pop_size
self.pr = pr
[docs] def create_solution(self) -> list:
"""
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, ...]], weight]
"""
position = np.random.uniform(self.problem.lb, self.problem.ub)
position = self.amend_position(position)
fitness = self.get_fitness_position(position)
weight = np.zeros(self.problem.n_dims)
return [position, fitness, weight]
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
# plus eps to avoid denominator zero
s = self.g_best[self.ID_TAR][self.ID_FIT] - self.pop[-1][self.ID_TAR][self.ID_FIT] + self.EPSILON
# calculate the fitness weight of each slime mold
for i in range(0, self.pop_size):
# Eq.(2.5)
if i <= int(self.pop_size / 2):
self.pop[i][self.ID_WEI] = 1 + np.random.uniform(0, 1, self.problem.n_dims) * \
np.log10((self.g_best[self.ID_TAR][self.ID_FIT] - self.pop[i][self.ID_TAR][self.ID_FIT]) / s + 1)
else:
self.pop[i][self.ID_WEI] = 1 - np.random.uniform(0, 1, self.problem.n_dims) * \
np.log10((self.g_best[self.ID_TAR][self.ID_FIT] - self.pop[i][self.ID_TAR][self.ID_FIT]) / s + 1)
a = np.arctanh(-((epoch + 1) / self.epoch) + 1) # Eq.(2.4)
b = 1 - (epoch + 1) / self.epoch
pop_new = []
for idx in range(0, self.pop_size):
# Update the Position of search agent
if np.random.uniform() < self.pr: # Eq.(2.7)
pos_new = np.random.uniform(self.problem.lb, self.problem.ub)
else:
p = np.tanh(np.abs(self.pop[idx][self.ID_TAR][self.ID_FIT] - self.g_best[self.ID_TAR][self.ID_FIT])) # Eq.(2.2)
vb = np.random.uniform(-a, a, self.problem.n_dims) # Eq.(2.3)
vc = np.random.uniform(-b, b, self.problem.n_dims)
# two positions randomly selected from population, apply for the whole problem size instead of 1 variable
id_a, id_b = np.random.choice(list(set(range(0, self.pop_size)) - {idx}), 2, replace=False)
pos_1 = self.g_best[self.ID_POS] + vb * (self.pop[idx][self.ID_WEI] * self.pop[id_a][self.ID_POS] - self.pop[id_b][self.ID_POS])
pos_2 = vc * self.pop[idx][self.ID_POS]
pos_new = np.where(np.random.uniform(0, 1, self.problem.n_dims) < p, pos_1, pos_2)
# Check bound and re-calculate fitness after each individual move
pos_new = self.amend_position(pos_new)
pop_new.append([pos_new, None, np.zeros(self.problem.n_dims)])
self.pop = self.update_fitness_population(pop_new)
[docs]class OriginalSMA(BaseSMA):
"""
The original version of: Slime Mould Algorithm (SMA)
Links:
1. https://doi.org/10.1016/j.future.2020.03.055
2. https://www.researchgate.net/publication/340431861_Slime_mould_algorithm_A_new_method_for_stochastic_optimization
Hyper-parameters should fine tuned in approximate range to get faster convergen toward the global optimum:
+ pr (float): [0.01, 0.1], probability threshold (z in the paper)
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy.bio_based.SMA import OriginalSMA
>>>
>>> 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
>>> pr = 0.03
>>> model = OriginalSMA(problem_dict1, epoch, pop_size, pr)
>>> best_position, best_fitness = model.solve()
>>> print(f"Solution: {best_position}, Fitness: {best_fitness}")
References
~~~~~~~~~~
[1] Li, S., Chen, H., Wang, M., Heidari, A.A. and Mirjalili, S., 2020. Slime mould algorithm: A new method for
stochastic optimization. Future Generation Computer Systems, 111, pp.300-323.
"""
ID_WEI = 2
def __init__(self, problem, epoch=10000, pop_size=100, pr=0.03, **kwargs):
"""
Args:
problem (dict): The problem dictionary
epoch (int): maximum number of iterations, default = 1000
pop_size (int): number of population size, default = 100
pr (float): probability threshold (z in the paper), default = 0.03
"""
super().__init__(problem, epoch, pop_size, pr, **kwargs)
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
# plus eps to avoid denominator zero
s = self.g_best[self.ID_TAR][self.ID_FIT] - self.pop[-1][self.ID_TAR][self.ID_FIT] + self.EPSILON
# calculate the fitness weight of each slime mold
for i in range(0, self.pop_size):
# Eq.(2.5)
if i <= int(self.pop_size / 2):
self.pop[i][self.ID_WEI] = 1 + np.random.uniform(0, 1, self.problem.n_dims) * \
np.log10((self.g_best[self.ID_TAR][self.ID_FIT] - self.pop[i][self.ID_TAR][self.ID_FIT]) / s + 1)
else:
self.pop[i][self.ID_WEI] = 1 - np.random.uniform(0, 1, self.problem.n_dims) * \
np.log10((self.g_best[self.ID_TAR][self.ID_FIT] - self.pop[i][self.ID_TAR][self.ID_FIT]) / s + 1)
a = np.arctanh(-((epoch + 1) / self.epoch) + 1) # Eq.(2.4)
b = 1 - (epoch + 1) / self.epoch
pop_new = []
for idx in range(0, self.pop_size):
# Update the Position of search agent
current_agent = deepcopy(self.pop[idx])
if np.random.uniform() < self.pr: # Eq.(2.7)
current_agent[self.ID_POS] = np.random.uniform(self.problem.lb, self.problem.ub)
else:
p = np.tanh(np.abs(current_agent[self.ID_TAR][self.ID_FIT] - self.g_best[self.ID_TAR][self.ID_FIT])) # Eq.(2.2)
vb = np.random.uniform(-a, a, self.problem.n_dims) # Eq.(2.3)
vc = np.random.uniform(-b, b, self.problem.n_dims)
for j in range(0, self.problem.n_dims):
# two positions randomly selected from population
id_a, id_b = np.random.choice(list(set(range(0, self.pop_size)) - {idx}), 2, replace=False)
if np.random.uniform() < p: # Eq.(2.1)
current_agent[self.ID_POS][j] = self.g_best[self.ID_POS][j] + \
vb[j] * (current_agent[self.ID_WEI][j] * self.pop[id_a][self.ID_POS][j] - self.pop[id_b][self.ID_POS][j])
else:
current_agent[self.ID_POS][j] = vc[j] * current_agent[self.ID_POS][j]
pos_new = self.amend_position(current_agent[self.ID_POS])
pop_new.append([pos_new, None, np.zeros(self.problem.n_dims)])
self.pop = self.update_fitness_population(pop_new)