#!/usr/bin/env python
# Created by "Thieu" at 10:14, 18/03/2020 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import numpy as np
from functools import reduce
from mealpy.optimizer import Optimizer
[docs]class DevTLO(Optimizer):
"""
The developed version: Teaching Learning-based Optimization (TLO)
Links:
1. https://doi.org/10.5267/j.ijiec.2012.03.007
Notes:
+ Use numpy np.array to make operations faster
+ The global best solution is used
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, TLO
>>>
>>> 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 = TLO.DevTLO(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] Rao, R. and Patel, V., 2012. An elitist teaching-learning-based optimization algorithm for solving
complex constrained optimization problems. international journal of industrial engineering computations, 3(4), pp.535-560.
"""
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 = False
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
pop_new = []
for idx in range(0, self.pop_size):
## Teaching Phrase
TF = self.generator.integers(1, 3) # 1 or 2 (never 3)
list_pos = np.array([agent.solution for agent in self.pop])
DIFF_MEAN = self.generator.random(self.problem.n_dims) * (self.g_best.solution - TF * np.mean(list_pos, axis=0))
pos_new = self.pop[idx].solution + DIFF_MEAN
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 = self.greedy_selection_population(self.pop, pop_new, self.problem.minmax)
pop_child = []
for idx in range(0, self.pop_size):
## Learning Phrase
pos_new = self.pop[idx].solution.copy().astype(float)
id_partner = self.generator.choice(np.setxor1d(np.array(range(self.pop_size)), np.array([idx])))
if self.compare_target(self.pop[idx].target, self.pop[id_partner].target, self.problem.minmax):
pos_new += self.generator.random(self.problem.n_dims) * (self.pop[idx].solution - self.pop[id_partner].solution)
else:
pos_new += self.generator.random(self.problem.n_dims) * (self.pop[id_partner].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)
[docs]class OriginalTLO(DevTLO):
"""
The original version of: Teaching Learning-based Optimization (TLO)
Notes:
+ Third loops are removed
+ This version is inspired from above link
+ https://github.com/andaviaco/tblo
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, TLO
>>>
>>> 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 = TLO.OriginalTLO(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] Rao, R.V., Savsani, V.J. and Vakharia, D.P., 2011. Teaching–learning-based optimization: a novel method
for constrained mechanical design optimization problems. Computer-aided design, 43(3), pp.303-315.
"""
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)
self.is_parallelizable = False
self.sort_flag = False
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
for idx in range(0, self.pop_size):
## Teaching Phrase
TF = self.generator.integers(1, 3) # 1 or 2 (never 3)
#### Remove third loop here
list_pos = np.array([agent.solution for agent in self.pop])
pos_new = self.pop[idx].solution + self.generator.uniform(0, 1, self.problem.n_dims) * \
(self.g_best.solution - TF * np.mean(list_pos, axis=0))
pos_new = self.correct_solution(pos_new)
agent = self.generate_agent(pos_new)
if self.compare_target(agent.target, self.pop[idx].target, self.problem.minmax):
self.pop[idx] = agent
## Learning Phrase
id_partner = self.generator.choice(np.setxor1d(np.array(range(self.pop_size)), np.array([idx])))
#### Remove third loop here
if self.compare_target(self.pop[idx].target, self.pop[id_partner].target, self.problem.minmax):
diff = self.pop[idx].solution - self.pop[id_partner].solution
else:
diff = self.pop[id_partner].solution - self.pop[idx].solution
pos_new = self.pop[idx].solution + self.generator.uniform(0, 1, self.problem.n_dims) * diff
pos_new = self.correct_solution(pos_new)
agent = self.generate_agent(pos_new)
if self.compare_target(agent.target, self.pop[idx].target, self.problem.minmax):
self.pop[idx] = agent
[docs]class ImprovedTLO(DevTLO):
"""
The original version of: Improved Teaching-Learning-based Optimization (ImprovedTLO)
Links:
1. https://doi.org/10.1016/j.scient.2012.12.005
Hyper-parameters should fine-tune in approximate range to get faster convergence toward the global optimum:
+ n_teachers (int): [3, 10], number of teachers in class, default=5
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, TLO
>>>
>>> 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 = TLO.ImprovedTLO(epoch=1000, pop_size=50, n_teachers = 5)
>>> 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] Rao, R.V. and Patel, V., 2013. An improved teaching-learning-based optimization algorithm
for solving unconstrained optimization problems. Scientia Iranica, 20(3), pp.710-720.
"""
def __init__(self, epoch: int = 10000, pop_size: int = 100, n_teachers: int = 5, **kwargs: object) -> None:
"""
Args:
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size, default = 100
n_teachers (int): number of teachers in class
"""
super().__init__(epoch, pop_size, **kwargs)
self.n_teachers = self.validator.check_int("n_teachers", n_teachers, [2, int(np.sqrt(self.pop_size)-1)])
self.set_parameters(["epoch", "pop_size", "n_teachers"])
self.n_students = self.pop_size - self.n_teachers
self.n_students_in_team = int(self.n_students / self.n_teachers)
self.sort_flag = False
[docs] def initialization(self):
if self.pop is None:
self.pop = self.generate_population(self.pop_size)
sorted_pop = self.get_sorted_population(self.pop, self.problem.minmax)
self.g_best = sorted_pop[0].copy()
self.teachers = sorted_pop[:self.n_teachers].copy()
sorted_pop = sorted_pop[self.n_teachers:]
idx_list = self.generator.permutation(range(0, self.n_students))
self.teams = []
for id_teacher in range(0, self.n_teachers):
group = []
for idx in range(0, self.n_students_in_team):
start_index = id_teacher * self.n_students_in_team + idx
group.append(sorted_pop[idx_list[start_index]])
self.teams.append(group)
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
for id_teach, teacher in enumerate(self.teachers):
team = self.teams[id_teach]
list_pos = np.array([student.solution for student in self.teams[id_teach]]) # Step 7
mean_team = np.mean(list_pos, axis=0)
pop_new = []
for id_stud, student in enumerate(team):
if teacher.target.fitness == 0:
TF = 1
else:
TF = student.target.fitness / teacher.target.fitness
diff_mean = self.generator.random() * (teacher.solution - TF * mean_team) # Step 8
id2 = self.generator.choice(list(set(range(0, self.n_teachers)) - {id_teach}))
if self.compare_target(teacher.target, team[id2].target, self.problem.minmax):
pos_new = (student.solution + diff_mean) + self.generator.random() * (team[id2].solution - student.solution)
else:
pos_new = (student.solution + diff_mean) + self.generator.random() * (student.solution - team[id2].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)
pop_new[-1] = self.get_better_agent(agent, student, self.problem.minmax)
if self.mode in self.AVAILABLE_MODES:
pop_new = self.update_target_for_population(pop_new)
pop_new = self.greedy_selection_population(team, pop_new, self.problem.minmax)
self.teams[id_teach] = pop_new
for id_teach, teacher in enumerate(self.teachers):
ef = round(1 + self.generator.random())
team = self.teams[id_teach]
pop_new = []
for id_stud, student in enumerate(team):
id2 = self.generator.choice(list(set(range(0, self.n_students_in_team)) - {id_stud}))
if self.compare_target(student.target, team[id2].target, self.problem.minmax):
pos_new = student.solution + self.generator.random() * (student.solution - team[id2].solution) + \
self.generator.random() * (teacher.solution - ef * team[id2].solution)
else:
pos_new = student.solution + self.generator.random() * (team[id2].solution - student.solution) + \
self.generator.random() * (teacher.solution - ef * student.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)
pop_new[-1] = self.get_better_agent(agent, student, self.problem.minmax)
if self.mode in self.AVAILABLE_MODES:
pop_new = self.update_target_for_population(pop_new)
pop_new = self.greedy_selection_population(team, pop_new, self.problem.minmax)
self.teams[id_teach] = pop_new
for id_teach, teacher in enumerate(self.teachers):
team = self.teams[id_teach] + [teacher]
team = self.get_sorted_population(team, self.problem.minmax)
self.teachers[id_teach] = team[0].copy()
self.teams[id_teach] = team[1:]
self.pop = self.teachers + reduce(lambda x, y: x + y, self.teams)