#!/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 DevJA(Optimizer):
"""
The developed version: Jaya Algorithm (JA)
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, JA
>>>
>>> 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 = JA.DevJA(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., 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, 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
"""
_, (g_best, ), (g_worst, ) = self.get_special_agents(self.pop, n_best=1, n_worst=1, minmax=self.problem.minmax)
pop_new = []
for idx in range(0, self.pop_size):
pos_new = self.pop[idx].solution + self.generator.random(self.problem.n_dims) * (g_best.solution - np.abs(self.pop[idx].solution)) + \
self.generator.normal() * (g_worst.solution - np.abs(self.pop[idx].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(self.pop[idx], agent, 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)
[docs]class OriginalJA(DevJA):
"""
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 import FloatVar, JA
>>>
>>> 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 = JA.OriginalJA(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., 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, 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
"""
_, (g_best, ), (g_worst, ) = self.get_special_agents(self.pop, n_best=1, n_worst=1, minmax=self.problem.minmax)
pop_new = []
for idx in range(0, self.pop_size):
pos_new = self.pop[idx].solution + self.generator.uniform(0, 1, self.problem.n_dims) * \
(g_best.solution - np.abs(self.pop[idx].solution)) - \
self.generator.uniform(0, 1, self.problem.n_dims) * (g_worst.solution - np.abs(self.pop[idx].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:
pop_new[idx].target = self.get_target(pos_new)
if self.mode in self.AVAILABLE_MODES:
pop_new = self.update_target_for_population(pop_new)
self.pop = pop_new
[docs]class LevyJA(DevJA):
"""
The original version of: Levy-flight Jaya Algorithm (LJA)
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.
+ https://doi.org/10.1016/j.eswa.2020.113902
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, JA
>>>
>>> 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 = JA.LevyJA(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] 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, 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.sort_flag = False
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
_, (g_best, ), (g_worst, ) = self.get_special_agents(self.pop, n_best=1, n_worst=1, minmax=self.problem.minmax)
pop_new = []
for idx in range(0, self.pop_size):
L1 = self.get_levy_flight_step(multiplier=1.0, beta=1.8, case=-1)
L2 = self.get_levy_flight_step(multiplier=1.0, beta=1.8, case=-1)
pos_new = self.pop[idx].solution + np.abs(L1) * (g_best.solution - np.abs(self.pop[idx].solution)) - \
np.abs(L2) * (g_worst.solution - np.abs(self.pop[idx].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(self.pop[idx], agent, 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)