#!/usr/bin/env python
# Created by "Thieu" at 11:16, 18/03/2020 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import numpy as np
from copy import deepcopy
from mealpy.optimizer import Optimizer
[docs]class OriginalLCO(Optimizer):
"""
The original version of: Life Choice-based Optimization (LCO)
Links:
1. https://doi.org/10.1007/s00500-019-04443-z
Hyper-parameters should fine tuned in approximate range to get faster convergen toward the global optimum:
+ r1 (float): [1.5, 4], coefficient factor, default = 2.35
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy.human_based.LCO import OriginalLCO
>>>
>>> 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
>>> r1 = 2.35
>>> model = OriginalLCO(problem_dict1, epoch, pop_size, r1)
>>> best_position, best_fitness = model.solve()
>>> print(f"Solution: {best_position}, Fitness: {best_fitness}")
References
~~~~~~~~~~
[1] Khatri, A., Gaba, A., Rana, K.P.S. and Kumar, V., 2020. A novel life choice-based optimizer. Soft Computing, 24(12), pp.9121-9141.
"""
def __init__(self, problem, epoch=10000, pop_size=100, r1=2.35, **kwargs):
"""
Args:
problem (dict): The problem dictionary
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size, default = 100
r1 (float): coefficient factor
"""
super().__init__(problem, kwargs)
self.nfe_per_epoch = pop_size
self.sort_flag = True
self.epoch = epoch
self.pop_size = pop_size
self.r1 = r1
self.n_agents = int(np.ceil(np.sqrt(self.pop_size)))
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
pop_new = []
for i in range(0, self.pop_size):
rand_number = np.random.random()
if rand_number > 0.875: # Update using Eq. 1, update from n best position
temp = np.array([np.random.random() * self.pop[j][self.ID_POS] for j in range(0, self.n_agents)])
temp = np.mean(temp, axis=0)
elif rand_number < 0.7: # Update using Eq. 2-6
f1 = 1 - epoch / self.epoch
f2 = 1 - f1
if i == 0:
pop_new.append(deepcopy(self.g_best))
continue
else:
best_diff = f1 * self.r1 * (self.g_best[self.ID_POS] - self.pop[i][self.ID_POS])
better_diff = f2 * self.r1 * (self.pop[i - 1][self.ID_POS] - self.pop[i][self.ID_POS])
temp = self.pop[i][self.ID_POS] + np.random.random() * better_diff + np.random.random() * best_diff
else:
temp = self.problem.ub - (self.pop[i][self.ID_POS] - self.problem.lb) * np.random.random()
pos_new = self.amend_position(temp)
pop_new.append([pos_new, None])
self.pop = self.update_fitness_population(pop_new)
[docs]class BaseLCO(OriginalLCO):
"""
My changed version of: Life Choice-based Optimization (LCO)
Notes
~~~~~
I only change the flow with simpler if else statement than the original
Hyper-parameters should fine tuned in approximate range to get faster convergen toward the global optimum:
+ r1 (float): [1.5, 4], coefficient factor, default = 2.35
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy.human_based.LCO import BaseLCO
>>>
>>> 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
>>> r1 = 2.35
>>> model = BaseLCO(problem_dict1, epoch, pop_size, r1)
>>> best_position, best_fitness = model.solve()
>>> print(f"Solution: {best_position}, Fitness: {best_fitness}")
"""
def __init__(self, problem, epoch=10000, pop_size=100, r1=2.35, **kwargs):
"""
Args:
problem (dict): The problem dictionary
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size, default = 100
r1 (float): coefficient factor
"""
super().__init__(problem, epoch, pop_size, r1, **kwargs)
self.nfe_per_epoch = pop_size
self.sort_flag = True
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
# epoch: current chance, self.epoch: number of chances
pop_new = []
for i in range(0, self.pop_size):
rand = np.random.random()
if rand > 0.875: # Update using Eq. 1, update from n best position
temp = np.array([np.random.random() * self.pop[j][self.ID_POS] for j in range(0, self.n_agents)])
temp = np.mean(temp, axis=0)
elif rand < 0.7: # Update using Eq. 2-6
f = (epoch + 1) / self.epoch
if i != 0:
better_diff = f * self.r1 * (self.pop[i - 1][self.ID_POS] - self.pop[i][self.ID_POS])
else:
better_diff = f * self.r1 * (self.g_best[self.ID_POS] - self.pop[i][self.ID_POS])
best_diff = (1 - f) * self.r1 * (self.pop[0][self.ID_POS] - self.pop[i][self.ID_POS])
temp = self.pop[i][self.ID_POS] + np.random.uniform() * better_diff + np.random.uniform() * best_diff
else:
temp = self.problem.ub - (self.pop[i][self.ID_POS] - self.problem.lb) * np.random.uniform(self.problem.lb, self.problem.ub)
pos_new = self.amend_position(temp)
pop_new.append([pos_new, None])
self.pop = self.update_fitness_population(pop_new)
[docs]class ImprovedLCO(Optimizer):
"""
My improved version of: Life Choice-based Optimization (ILCO)
Notes
~~~~~
+ The flow of the original LCO is kept.
+ Add gaussian distribution and mutation mechanism
+ Remove the hyper-parameter r1
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy.human_based.LCO import BaseLCO
>>>
>>> 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 = BaseLCO(problem_dict1, epoch, pop_size)
>>> best_position, best_fitness = model.solve()
>>> print(f"Solution: {best_position}, Fitness: {best_fitness}")
"""
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 = 2 * pop_size
self.sort_flag = True
self.epoch = epoch
self.pop_size = pop_size
self.pop_len = int(self.pop_size / 2)
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
# epoch: current chance, self.epoch: number of chances
pop_new = []
for i in range(0, self.pop_size):
rand = np.random.random()
if rand > 0.875: # Update using Eq. 1, update from n best position
n = int(np.ceil(np.sqrt(self.pop_size)))
pos_new = np.array([np.random.uniform() * self.pop[j][self.ID_POS] for j in range(0, n)])
pos_new = np.mean(pos_new, axis=0)
elif rand < 0.7: # Update using Eq. 2-6
f = (epoch + 1) / self.epoch
if i != 0:
better_diff = f * np.random.uniform() * (self.pop[i - 1][self.ID_POS] - self.pop[i][self.ID_POS])
else:
better_diff = f * np.random.uniform() * (self.g_best[self.ID_POS] - self.pop[i][self.ID_POS])
best_diff = (1 - f) * np.random.uniform() * (self.pop[0][self.ID_POS] - self.pop[i][self.ID_POS])
pos_new = self.pop[i][self.ID_POS] + better_diff + best_diff
else:
pos_new = self.problem.ub - (self.pop[i][self.ID_POS] - self.problem.lb) * np.random.uniform(self.problem.lb, self.problem.ub)
pos_new = self.amend_position(pos_new)
pop_new.append([pos_new, None])
pop_new = self.update_fitness_population(pop_new)
## Sort the updated population based on fitness
pop, local_best = self.get_global_best_solution(pop_new)
pop_s1, pop_s2 = pop[:self.pop_len], pop[self.pop_len:]
## Mutation scheme
for i in range(0, self.pop_len):
pos_new = pop_s1[i][self.ID_POS] * (1 + np.random.normal(0, 1, self.problem.n_dims))
pop_s1[i][self.ID_POS] = self.amend_position(pos_new)
pop_s1 = self.update_fitness_population(pop_s1)
## Search Mechanism
pos_s1_list = [item[self.ID_POS] for item in pop_s1]
pos_s1_mean = np.mean(pos_s1_list, axis=0)
for i in range(0, self.pop_len):
pos_new = (local_best[self.ID_POS] - pos_s1_mean) - np.random.random() * \
(self.problem.lb + np.random.random() * (self.problem.ub - self.problem.lb))
pop_s2[i][self.ID_POS] = self.amend_position(pos_new)
pop_s2 = self.update_fitness_population(pop_s2)
## Construct a new population
self.pop = pop_s1 + pop_s2