# !/usr/bin/env python
# Created by "Thieu" at 16:30, 16/11/2020 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import numpy as np
from mealpy.optimizer import Optimizer
[docs]class BaseJA(Optimizer):
"""
My changed version of: Jaya Algorithm (JA)
Notes
~~~~~
+ All third loops are removed
+ Change the second random variable r2 to Gaussian instead of Uniform
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy.swarm_based.JA import BaseJA
>>>
>>> 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 = BaseJA(problem_dict1, epoch, pop_size)
>>> best_position, best_fitness = model.solve()
>>> print(f"Solution: {best_position}, Fitness: {best_fitness}")
References
~~~~~~~~~~
[1] Rao, R., 2016. Jaya: A simple and new optimization algorithm for solving constrained and
unconstrained optimization problems. International Journal of Industrial Engineering Computations, 7(1), pp.19-34.
"""
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 = pop_size
self.sort_flag = False
self.epoch = epoch
self.pop_size = pop_size
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
_, best, worst = self.get_special_solutions(self.pop, best=1, worst=1)
g_best, g_worst = best[0], worst[0]
pop_new = []
for idx in range(0, self.pop_size):
pos_new = self.pop[idx][self.ID_POS] + np.random.uniform() * (g_best[self.ID_POS] - np.abs(self.pop[idx][self.ID_POS])) + \
np.random.normal() * (g_worst[self.ID_POS] - np.abs(self.pop[idx][self.ID_POS]))
pos_new = self.amend_position(pos_new)
pop_new.append([pos_new, None])
self.pop = self.update_fitness_population(pop_new)
[docs]class OriginalJA(BaseJA):
"""
The original version of: Jaya Algorithm (JA)
Links:
1. https://www.growingscience.com/ijiec/Vol7/IJIEC_2015_32.pdf
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy.swarm_based.JA import OriginalJA
>>>
>>> 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 = OriginalJA(problem_dict1, epoch, pop_size)
>>> best_position, best_fitness = model.solve()
>>> print(f"Solution: {best_position}, Fitness: {best_fitness}")
References
~~~~~~~~~~
[1] Rao, R., 2016. Jaya: A simple and new optimization algorithm for solving constrained and
unconstrained optimization problems. International Journal of Industrial Engineering Computations, 7(1), pp.19-34.
"""
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, 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
"""
_, best, worst = self.get_special_solutions(self.pop, best=1, worst=1)
g_best, g_worst = best[0], worst[0]
pop_new = []
for idx in range(0, self.pop_size):
pos_new = self.pop[idx][self.ID_POS] + np.random.uniform(0, 1, self.problem.n_dims) * \
(g_best[self.ID_POS] - np.abs(self.pop[idx][self.ID_POS])) - \
np.random.uniform(0, 1, self.problem.n_dims) * (g_worst[self.ID_POS] - np.abs(self.pop[idx][self.ID_POS]))
pos_new = self.amend_position(pos_new)
pop_new.append([pos_new, None])
self.pop = self.update_fitness_population(pop_new)
[docs]class LevyJA(BaseJA):
"""
The original version of: Levy-flight Jaya Algorithm (LJA)
Links:
1. https://doi.org/10.1016/j.eswa.2020.113902
Notes
~~~~~
+ All third loops in this version also are removed
+ The beta value of Levy-flight equal to 1.8 as the best value in the paper.
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy.swarm_based.JA import LevyJA
>>>
>>> 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 = LevyJA(problem_dict1, epoch, pop_size)
>>> best_position, best_fitness = model.solve()
>>> print(f"Solution: {best_position}, Fitness: {best_fitness}")
References
~~~~~~~~~~
[1] Iacca, G., dos Santos Junior, V.C. and de Melo, V.V., 2021. An improved Jaya optimization
algorithm with Lévy flight. Expert Systems with Applications, 165, p.113902.
"""
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, epoch, pop_size, **kwargs)
self.nfe_per_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
"""
_, best, worst = self.get_special_solutions(self.pop, best=1, worst=1)
g_best, g_worst = best[0], worst[0]
pop_new = []
for idx in range(0, self.pop_size):
L1 = self.get_levy_flight_step(multiplier=1.0, beta=1.0, case=-1)
L2 = self.get_levy_flight_step(multiplier=1.0, beta=1.0, case=-1)
pos_new = self.pop[idx][self.ID_POS] + np.abs(L1) * (g_best[self.ID_POS] - np.abs(self.pop[idx][self.ID_POS])) - \
np.abs(L2) * (g_worst[self.ID_POS] - np.abs(self.pop[idx][self.ID_POS]))
pos_new = self.amend_position(pos_new)
pop_new.append([pos_new, None])
self.pop = self.update_fitness_population(pop_new)