#!/usr/bin/env python
# Created by "Thieu" at 21:45, 13/03/2023 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import numpy as np
from mealpy.optimizer import Optimizer
[docs]class OriginalCDO(Optimizer):
"""
The original version of: Chernobyl Disaster Optimizer (CDO)
Links:
1. https://link.springer.com/article/10.1007/s00521-023-08261-1
2. https://www.mathworks.com/matlabcentral/fileexchange/124351-chernobyl-disaster-optimizer-cdo
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, CDO
>>>
>>> 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 = CDO.OriginalCDO(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] Shehadeh, H. A. (2023). Chernobyl disaster optimizer (CDO): a novel meta-heuristic method
for global optimization. Neural Computing and Applications, 1-17.
"""
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
"""
_, (b1, b2, b3), _ = self.get_special_agents(self.pop, n_best=3, n_worst=1, minmax=self.problem.minmax)
a = 3. - 3.*epoch/self.epoch
a1 = np.log10((16000-1) * self.generator.random() + 16000)
a2 = np.log10((270000-1) * self.generator.random() + 270000)
a3 = np.log10((300000-1) * self.generator.random() + 300000)
pop_new = []
for idx in range(0, self.pop_size):
r1 = self.generator.random(self.problem.n_dims)
r2 = self.generator.random(self.problem.n_dims)
pa = np.pi * r1*r1 / (0.25 * a1) - a * self.generator.random(self.problem.n_dims)
c1 = r2 * r2 * np.pi
alpha = np.abs(c1*b1.solution - self.pop[idx].solution)
pos_a = 0.25 * (b1.solution - pa * alpha)
r3 = self.generator.random(self.problem.n_dims)
r4 = self.generator.random(self.problem.n_dims)
pb = np.pi * r3 * r3 / (0.5 * a2) - a * self.generator.random(self.problem.n_dims)
c2 = r4 * r4 * np.pi
beta = np.abs(c2 * b2.solution - self.pop[idx].solution)
pos_b = 0.5 * (b2.solution - pb * beta)
r5 = self.generator.random(self.problem.n_dims)
r6 = self.generator.random(self.problem.n_dims)
pc = np.pi * r5 * r5 / a3 - a * self.generator.random(self.problem.n_dims)
c3 = r6 * r6 * np.pi
gama = np.abs(c3 * b3.solution - self.pop[idx].solution)
pos_c = b3.solution - pc * gama
pos_new = (pos_a + pos_b + pos_c) / 3
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[-1].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