#!/usr/bin/env python
# Created by "Thieu" at 16:44, 18/03/2020 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import numpy as np
from mealpy.optimizer import Optimizer
[docs]class OriginalAEO(Optimizer):
"""
The original version of: Artificial Ecosystem-based Optimization (AEO)
Links:
1. https://doi.org/10.1007/s00521-019-04452-x
2. https://www.mathworks.com/matlabcentral/fileexchange/72685-artificial-ecosystem-based-optimization-aeo
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, AEO
>>>
>>> def objective_function(solution):
>>> return np.sum(solution**2)
>>>
>>> problem_dict = {
>>> "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
>>> "minmax": "min",
>>> "obj_func": objective_function
>>> }
>>>
>>> model = AEO.OriginalAEO(epoch=1000, pop_size=50)
>>> g_best = model.solve(problem_dict)
>>> print(f"Solution: {g_best.solution}, Fitness: {g_best.target.fitness}")
>>> print(f"Solution: {model.g_best.solution}, Fitness: {model.g_best.target.fitness}")
References
~~~~~~~~~~
[1] Zhao, W., Wang, L. and Zhang, Z., 2020. Artificial ecosystem-based optimization: a novel
nature-inspired meta-heuristic algorithm. Neural Computing and Applications, 32(13), pp.9383-9425.
"""
def __init__(self, epoch: int = 10000, pop_size: int = 100, **kwargs: object) -> None:
"""
Args:
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size, default = 100
"""
super().__init__(**kwargs)
self.epoch = self.validator.check_int("epoch", epoch, [1, 100000])
self.pop_size = self.validator.check_int("pop_size", pop_size, [5, 10000])
self.set_parameters(["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
"""
## Production - Update the worst agent
# Eq. 2, 3, 1
a = (1.0 - epoch / self.epoch) * self.generator.uniform()
x1 = (1 - a) * self.pop[-1].solution + a * self.generator.uniform(self.problem.lb, self.problem.ub)
pos_new = self.correct_solution(x1)
agent = self.generate_agent(pos_new)
self.pop[-1] = agent
## Consumption - Update the whole population left
pop_new = []
for idx in range(0, self.pop_size - 1):
rand = self.generator.random()
# Eq. 4, 5, 6
v1 = self.generator.normal(0, 1)
v2 = self.generator.normal(0, 1)
c = 0.5 * v1 / abs(v2) # Consumption factor
jdx = 1 if idx == 0 else self.generator.integers(0, idx)
### Herbivore
if rand < 1.0 / 3:
x_t1 = self.pop[idx].solution + c * (self.pop[idx].solution - self.pop[0].solution) # Eq. 6
### Carnivore
elif 1.0 / 3 <= rand and rand <= 2.0 / 3:
x_t1 = self.pop[idx].solution + c * (self.pop[idx].solution - self.pop[jdx].solution) # Eq. 7
### Omnivore
else:
r2 = self.generator.uniform()
x_t1 = self.pop[idx].solution + c * (r2 * (self.pop[idx].solution - self.pop[0].solution)
+ (1 - r2) * (self.pop[idx].solution - self.pop[jdx].solution))
pos_new = self.correct_solution(x_t1)
agent = self.generate_empty_agent(pos_new)
pop_new.append(agent)
if self.mode not in self.AVAILABLE_MODES:
agent.target = self.get_target(pos_new)
self.pop[idx] = self.get_better_agent(agent, self.pop[idx], self.problem.minmax)
if self.mode in self.AVAILABLE_MODES:
pop_new = self.update_target_for_population(pop_new)
self.pop[:-1] = self.greedy_selection_population(self.pop[:-1], pop_new, self.problem.minmax)
## find current best used in decomposition
best = self.get_best_agent(self.pop, self.problem.minmax)
## Decomposition
### Eq. 10, 11, 12, 9
pop_child = []
for idx in range(0, self.pop_size):
r3 = self.generator.uniform()
d = 3 * self.generator.normal(0, 1)
e = r3 * self.generator.integers(1, 3) - 1
h = 2 * r3 - 1
x_t1 = best.solution + d * (e * best.solution - h * self.pop[idx].solution)
pos_new = self.correct_solution(x_t1)
agent = self.generate_empty_agent(pos_new)
pop_child.append(agent)
if self.mode not in self.AVAILABLE_MODES:
agent.target = self.get_target(pos_new)
self.pop[idx] = self.get_better_agent(agent, self.pop[idx], self.problem.minmax)
if self.mode in self.AVAILABLE_MODES:
pop_child = self.update_target_for_population(pop_child)
self.pop = self.greedy_selection_population(self.pop, pop_child, self.problem.minmax)
[docs]class ImprovedAEO(OriginalAEO):
"""
The original version of: Improved Artificial Ecosystem-based Optimization (ImprovedAEO)
Links:
1. https://doi.org/10.1016/j.ijhydene.2020.06.256
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, AEO
>>>
>>> def objective_function(solution):
>>> return np.sum(solution**2)
>>>
>>> problem_dict = {
>>> "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
>>> "minmax": "min",
>>> "obj_func": objective_function
>>> }
>>>
>>> model = AEO.ImprovedAEO(epoch=1000, pop_size=50)
>>> g_best = model.solve(problem_dict)
>>> print(f"Solution: {g_best.solution}, Fitness: {g_best.target.fitness}")
>>> print(f"Solution: {model.g_best.solution}, Fitness: {model.g_best.target.fitness}")
References
~~~~~~~~~~
[1] Rizk-Allah, R.M. and El-Fergany, A.A., 2021. Artificial ecosystem optimizer for parameters identification of
proton exchange membrane fuel cells model. International Journal of Hydrogen Energy, 46(75), pp.37612-37627.
"""
def __init__(self, epoch: int = 10000, pop_size: int = 100, **kwargs: object) -> None:
"""
Args:
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size, default = 100
"""
super().__init__(epoch, pop_size, **kwargs)
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
## Production - Update the worst agent
# Eq. 2, 3, 1
a = (1.0 - epoch / self.epoch) * self.generator.uniform()
x1 = (1 - a) * self.pop[-1].solution + a * self.generator.uniform(self.problem.lb, self.problem.ub)
pos_new = self.correct_solution(x1)
agent = self.generate_agent(pos_new)
self.pop[-1] = agent
## Consumption - Update the whole population left
pop_new = []
for idx in range(0, self.pop_size - 1):
rand = self.generator.random()
# Eq. 4, 5, 6
v1 = self.generator.normal(0, 1)
v2 = self.generator.normal(0, 1)
c = 0.5 * v1 / np.abs(v2) # Consumption factor
j = 1 if idx == 0 else self.generator.integers(0, idx)
### Herbivore
if rand < 1.0 / 3:
x_t1 = self.pop[idx].solution + c * (self.pop[idx].solution - self.pop[0].solution) # Eq. 6
### Carnivore
elif 1.0 / 3 <= rand and rand <= 2.0 / 3:
x_t1 = self.pop[idx].solution + c * (self.pop[idx].solution - self.pop[j].solution) # Eq. 7
### Omnivore
else:
r2 = self.generator.uniform()
x_t1 = self.pop[idx].solution + c * (r2 * (self.pop[idx].solution - self.pop[0].solution) +
(1 - r2) * (self.pop[idx].solution - self.pop[j].solution))
pos_new = self.correct_solution(x_t1)
agent = self.generate_empty_agent(pos_new)
pop_new.append(agent)
if self.mode not in self.AVAILABLE_MODES:
agent.target = self.get_target(pos_new)
self.pop[idx] = self.get_better_agent(agent, self.pop[idx], self.problem.minmax)
if self.mode in self.AVAILABLE_MODES:
pop_new = self.update_target_for_population(pop_new)
self.pop[:-1] = self.greedy_selection_population(self.pop[:-1], pop_new, self.problem.minmax)
## find current best used in decomposition
best = self.get_best_agent(self.pop, self.problem.minmax)
## Decomposition
### Eq. 10, 11, 12, 9
pop_child = []
for idx in range(0, self.pop_size):
r3 = self.generator.uniform()
d = 3 * self.generator.normal(0, 1)
e = r3 * self.generator.integers(1, 3) - 1
h = 2 * r3 - 1
if self.generator.random() < 0.5:
beta = 1 - (1 - 0) * (epoch/ self.epoch) # Eq. 21
x_r = self.pop[self.generator.integers(0, self.pop_size - 1)].solution
if self.generator.random() < 0.5:
x_new = beta * x_r + (1 - beta) * self.pop[idx].solution
else:
x_new = beta * self.pop[idx].solution + (1 - beta) * x_r
else:
x_new = best.solution + d * (e * best.solution - h * self.pop[idx].solution)
# x_new = best.solution + self.generator.normal() * best.solution
pos_new = self.correct_solution(x_new)
agent = self.generate_empty_agent(pos_new)
pop_child.append(agent)
if self.mode not in self.AVAILABLE_MODES:
agent.target = self.get_target(pos_new)
self.pop[idx] = self.get_better_agent(agent, self.pop[idx], self.problem.minmax)
if self.mode in self.AVAILABLE_MODES:
pop_child = self.update_target_for_population(pop_child)
self.pop = self.greedy_selection_population(self.pop, pop_child, self.problem.minmax)
[docs]class EnhancedAEO(Optimizer):
"""
The original version of: Enhanced Artificial Ecosystem-Based Optimization (EAEO)
Links:
1. https://doi.org/10.1109/ACCESS.2020.3027654
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, AEO
>>>
>>> def objective_function(solution):
>>> return np.sum(solution**2)
>>>
>>> problem_dict = {
>>> "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
>>> "minmax": "min",
>>> "obj_func": objective_function
>>> }
>>>
>>> model = AEO.EnhancedAEO(epoch=1000, pop_size=50)
>>> g_best = model.solve(problem_dict)
>>> print(f"Solution: {g_best.solution}, Fitness: {g_best.target.fitness}")
>>> print(f"Solution: {model.g_best.solution}, Fitness: {model.g_best.target.fitness}")
References
~~~~~~~~~~
[1] Eid, A., Kamel, S., Korashy, A. and Khurshaid, T., 2020. An enhanced artificial ecosystem-based
optimization for optimal allocation of multiple distributed generations. IEEE Access, 8, pp.178493-178513.
"""
def __init__(self, epoch: int = 10000, pop_size: int = 100, **kwargs: object) -> None:
"""
Args:
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size, default = 100
"""
super().__init__(**kwargs)
self.epoch = self.validator.check_int("epoch", epoch, [1, 100000])
self.pop_size = self.validator.check_int("pop_size", pop_size, [5, 10000])
self.set_parameters(["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
"""
## Production - Update the worst agent
# Eq. 13
a = 2 * (1. - epoch / self.epoch)
x1 = (1 - a) * self.pop[-1].solution + a * self.generator.uniform(self.problem.lb, self.problem.ub)
pos_new = self.correct_solution(x1)
agent = self.generate_agent(pos_new)
self.pop[-1] = agent
## Consumption - Update the whole population left
pop_new = []
for idx in range(0, self.pop_size - 1):
rand = self.generator.random()
# Eq. 4, 5, 6
v1 = self.generator.normal(0, 1)
v2 = self.generator.normal(0, 1)
c = 0.5 * v1 / abs(v2) # Consumption factor
r3 = 2 * np.pi * self.generator.random()
r4 = self.generator.random()
j = 1 if idx == 0 else self.generator.integers(0, idx)
### Herbivore
if rand <= 1.0 / 3: # Eq. 15
if r4 <= 0.5:
x_t1 = self.pop[idx].solution + np.sin(r3) * c * (self.pop[idx].solution - self.pop[0].solution)
else:
x_t1 = self.pop[idx].solution + np.cos(r3) * c * (self.pop[idx].solution - self.pop[0].solution)
### Carnivore
elif 1.0 / 3 <= rand and rand <= 2.0 / 3: # Eq. 16
if r4 <= 0.5:
x_t1 = self.pop[idx].solution + np.sin(r3) * c * (self.pop[idx].solution - self.pop[j].solution)
else:
x_t1 = self.pop[idx].solution + np.cos(r3) * c * (self.pop[idx].solution - self.pop[j].solution)
### Omnivore
else: # Eq. 17
r5 = self.generator.random()
if r4 <= 0.5:
x_t1 = self.pop[idx].solution + np.sin(r5) * c * (r5 * (self.pop[idx].solution - self.pop[0].solution) +
(1 - r5) * (self.pop[idx].solution - self.pop[j].solution))
else:
x_t1 = self.pop[idx].solution + np.cos(r5) * c * (r5 * (self.pop[idx].solution - self.pop[0].solution) +
(1 - r5) * (self.pop[idx].solution - self.pop[j].solution))
pos_new = self.correct_solution(x_t1)
agent = self.generate_empty_agent(pos_new)
pop_new.append(agent)
if self.mode not in self.AVAILABLE_MODES:
agent.target = self.get_target(pos_new)
self.pop[idx] = self.get_better_agent(agent, self.pop[idx], self.problem.minmax)
if self.mode in self.AVAILABLE_MODES:
pop_new = self.update_target_for_population(pop_new)
self.pop[:-1] = self.greedy_selection_population(self.pop[:-1], pop_new, self.problem.minmax)
## find current best used in decomposition
best = self.get_best_agent(self.pop, self.problem.minmax)
## Decomposition
### Eq. 10, 11, 12, 9
pop_child = []
for idx in range(0, self.pop_size):
r3 = self.generator.uniform()
d = 3 * self.generator.normal(0, 1)
e = r3 * self.generator.integers(1, 3) - 1
h = 2 * r3 - 1
if self.generator.random() < 0.5:
beta = 1 - (1 - 0) * (epoch / self.epoch) # Eq. 21
r_idx = self.generator.choice(list(set(range(0, self.pop_size)) - {idx}))
x_r = self.pop[r_idx].solution
if self.generator.random() < 0.5:
x_new = beta * x_r + (1 - beta) * self.pop[idx].solution
else:
x_new = (1 - beta) * x_r + beta * self.pop[idx].solution
else:
x_new = best.solution + d * (e * best.solution - h * self.pop[idx].solution)
pos_new = self.correct_solution(x_new)
agent = self.generate_empty_agent(pos_new)
pop_child.append(agent)
if self.mode not in self.AVAILABLE_MODES:
agent.target = self.get_target(pos_new)
self.pop[idx] = self.get_better_agent(agent, self.pop[idx], self.problem.minmax)
if self.mode in self.AVAILABLE_MODES:
pop_child = self.update_target_for_population(pop_child)
self.pop = self.greedy_selection_population(self.pop, pop_child, self.problem.minmax)
[docs]class ModifiedAEO(Optimizer):
"""
The original version of: Modified Artificial Ecosystem-Based Optimization (MAEO)
Links:
1. https://doi.org/10.1109/ACCESS.2020.2973351
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, AEO
>>>
>>> def objective_function(solution):
>>> return np.sum(solution**2)
>>>
>>> problem_dict = {
>>> "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
>>> "minmax": "min",
>>> "obj_func": objective_function
>>> }
>>>
>>> model = AEO.ModifiedAEO(epoch=1000, pop_size=50)
>>> g_best = model.solve(problem_dict)
>>> print(f"Solution: {g_best.solution}, Fitness: {g_best.target.fitness}")
>>> print(f"Solution: {model.g_best.solution}, Fitness: {model.g_best.target.fitness}")
References
~~~~~~~~~~
[1] Menesy, A.S., Sultan, H.M., Korashy, A., Banakhr, F.A., Ashmawy, M.G. and Kamel, S., 2020. Effective
parameter extraction of different polymer electrolyte membrane fuel cell stack models using a
modified artificial ecosystem optimization algorithm. IEEE Access, 8, pp.31892-31909.
"""
def __init__(self, epoch: int = 10000, pop_size: int = 100, **kwargs: object) -> None:
"""
Args:
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size, default = 100
"""
super().__init__(**kwargs)
self.epoch = self.validator.check_int("epoch", epoch, [1, 100000])
self.pop_size = self.validator.check_int("pop_size", pop_size, [5, 10000])
self.set_parameters(["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
"""
## Production
# Eq. 22
H = 2 * (1 - epoch / self.epoch)
a = (1 - epoch / self.epoch) * self.generator.random()
x1 = (1 - a) * self.pop[-1].solution + a * self.generator.uniform(self.problem.lb, self.problem.ub)
pos_new = self.correct_solution(x1)
agent = self.generate_agent(pos_new)
self.pop[-1] = agent
## Consumption - Update the whole population left
pop_new = []
for idx in range(0, self.pop_size - 1):
rand = self.generator.random()
# Eq. 4, 5, 6
v1 = self.generator.normal(0, 1)
v2 = self.generator.normal(0, 1)
c = 0.5 * v1 / abs(v2) # Consumption factor
j = 1 if idx == 0 else self.generator.integers(0, idx)
### Herbivore
if rand <= 1.0 / 3: # Eq. 23
pos_new = self.pop[idx].solution + H * c * (self.pop[idx].solution - self.pop[0].solution)
### Carnivore
elif 1.0 / 3 <= rand and rand <= 2.0 / 3: # Eq. 24
pos_new = self.pop[idx].solution + H * c * (self.pop[idx].solution - self.pop[j].solution)
### Omnivore
else: # Eq. 25
r5 = self.generator.random()
pos_new = self.pop[idx].solution + H * c * (r5 * (self.pop[idx].solution - self.pop[0].solution) +
(1 - r5) * (self.pop[idx].solution - self.pop[j].solution))
pos_new = self.correct_solution(pos_new)
agent = self.generate_empty_agent(pos_new)
pop_new.append(agent)
if self.mode not in self.AVAILABLE_MODES:
agent.target = self.get_target(pos_new)
self.pop[idx] = self.get_better_agent(agent, self.pop[idx], self.problem.minmax)
if self.mode in self.AVAILABLE_MODES:
pop_new = self.update_target_for_population(pop_new)
self.pop[:-1] = self.greedy_selection_population(self.pop[:-1], pop_new, self.problem.minmax)
## find current best used in decomposition
best = self.get_best_agent(self.pop, self.problem.minmax)
## Decomposition
### Eq. 10, 11, 12, 9
pop_child = []
for idx in range(0, self.pop_size):
r3 = self.generator.uniform()
d = 3 * self.generator.normal(0, 1)
e = r3 * self.generator.integers(1, 3) - 1
h = 2 * r3 - 1
if self.generator.random() < 0.5:
beta = 1 - (1 - 0) * (epoch / self.epoch) # Eq. 21
r_idx = self.generator.choice(list(set(range(0, self.pop_size)) - {idx}))
x_r = self.pop[r_idx].solution
if self.generator.random() < 0.5:
x_new = beta * x_r + (1 - beta) * self.pop[idx].solution
else:
x_new = (1 - beta) * x_r + beta * self.pop[idx].solution
else:
x_new = best.solution + d * (e * best.solution - h * self.pop[idx].solution)
pos_new = self.correct_solution(x_new)
agent = self.generate_empty_agent(pos_new)
pop_child.append(agent)
if self.mode not in self.AVAILABLE_MODES:
agent.target = self.get_target(pos_new)
self.pop[idx] = self.get_better_agent(agent, self.pop[idx], self.problem.minmax)
if self.mode in self.AVAILABLE_MODES:
pop_child = self.update_target_for_population(pop_child)
self.pop = self.greedy_selection_population(self.pop, pop_child, self.problem.minmax)
[docs]class AugmentedAEO(Optimizer):
"""
The original version of: Augmented Artificial Ecosystem Optimization (AAEO)
Notes:
+ Used linear weight factor reduce from 2 to 0 through time
+ Applied Levy-flight technique and the global best solution
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, AEO
>>>
>>> def objective_function(solution):
>>> return np.sum(solution**2)
>>>
>>> problem_dict = {
>>> "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
>>> "minmax": "min",
>>> "obj_func": objective_function
>>> }
>>>
>>> model = AEO.AugmentedAEO(epoch=1000, pop_size=50)
>>> g_best = model.solve(problem_dict)
>>> print(f"Solution: {g_best.solution}, Fitness: {g_best.target.fitness}")
>>> print(f"Solution: {model.g_best.solution}, Fitness: {model.g_best.target.fitness}")
References
~~~~~~~~~~
[1] Van Thieu, N., Barma, S. D., Van Lam, T., Kisi, O., & Mahesha, A. (2022). Groundwater level modeling
using Augmented Artificial Ecosystem Optimization. Journal of Hydrology, 129034.
"""
def __init__(self, epoch: int = 10000, pop_size: int = 100, **kwargs: object) -> None:
"""
Args:
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size, default = 100
"""
super().__init__(**kwargs)
self.epoch = self.validator.check_int("epoch", epoch, [1, 100000])
self.pop_size = self.validator.check_int("pop_size", pop_size, [5, 10000])
self.set_parameters(["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
"""
## Production - Update the worst agent
# Eq. 2, 3, 1
wf = 2 * (1 - epoch / self.epoch) # Weight factor
a = (1.0 - epoch / self.epoch) * self.generator.random()
x1 = (1 - a) * self.pop[-1].solution + a * self.generator.uniform(self.problem.lb, self.problem.ub)
pos_new = self.correct_solution(x1)
agent = self.generate_agent(pos_new)
self.pop[-1] = agent
## Consumption - Update the whole population left
pop_new = []
for idx in range(0, self.pop_size - 1):
if self.generator.random() < 0.5:
rand = self.generator.random()
# Eq. 4, 5, 6
c = 0.5 * self.generator.normal(0, 1) / np.abs(self.generator.normal(0, 1)) # Consumption factor
j = 1 if idx == 0 else self.generator.integers(0, idx)
### Herbivore
if rand < 1.0 / 3:
pos_new = self.pop[idx].solution + wf * c * (self.pop[idx].solution - self.pop[0].solution) # Eq. 6
### Omnivore
elif 1.0 / 3 <= rand <= 2.0 / 3:
pos_new = self.pop[idx].solution + wf * c * (self.pop[idx].solution - self.pop[j].solution) # Eq. 7
### Carnivore
else:
r2 = self.generator.uniform()
pos_new = self.pop[idx].solution + wf * c * (r2 * (self.pop[idx].solution - self.pop[0].solution) +
(1 - r2) * (self.pop[idx].solution - self.pop[j].solution))
else:
pos_new = self.pop[idx].solution + self.get_levy_flight_step(1., 0.001, case=-1) * \
(1.0 / np.sqrt(epoch)) * np.sign(self.generator.random() - 0.5) * (self.pop[idx].solution - self.g_best.solution)
pos_new = self.correct_solution(pos_new)
agent = self.generate_empty_agent(pos_new)
pop_new.append(agent)
if self.mode not in self.AVAILABLE_MODES:
agent.target = self.get_target(pos_new)
self.pop[idx] = self.get_better_agent(agent, self.pop[idx], self.problem.minmax)
if self.mode in self.AVAILABLE_MODES:
pop_new = self.update_target_for_population(pop_new)
self.pop[:-1] = self.greedy_selection_population(self.pop[:-1], pop_new, self.problem.minmax)
## find current best used in decomposition
best = self.get_best_agent(self.pop, self.problem.minmax)
## Decomposition
### Eq. 10, 11, 12, 9 idx, pop, g_best, local_best
pop_child = []
for idx in range(0, self.pop_size):
if self.generator.random() < 0.5:
pos_new = best.solution + self.generator.normal(0, 1, self.problem.n_dims) * (best.solution - self.pop[idx].solution)
else:
beta = self.generator.uniform(0.01, 1.)
pos_new = best.solution + self.get_levy_flight_step(beta=beta, multiplier=0.01, size=self.problem.n_dims, case=0) * (best.solution - self.pop[idx].solution)
pos_new = self.correct_solution(pos_new)
agent = self.generate_empty_agent(pos_new)
pop_child.append(agent)
if self.mode not in self.AVAILABLE_MODES:
agent.target = self.get_target(pos_new)
self.pop[idx] = self.get_better_agent(agent, self.pop[idx], self.problem.minmax)
if self.mode in self.AVAILABLE_MODES:
pop_child = self.update_target_for_population(pop_child)
self.pop = self.greedy_selection_population(self.pop, pop_child, self.problem.minmax)