#!/usr/bin/env python
# Created by "Thieu" at 17:38, 21/05/2022 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import numpy as np
from mealpy.optimizer import Optimizer
[docs]class OriginalCircleSA(Optimizer):
"""
The original version of: Circle Search Algorithm (CircleSA)
Links:
1. https://doi.org/10.3390/math10101626
2. https://www.mdpi.com/2227-7390/10/10/1626
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, CircleSA
>>>
>>> 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 = CircleSA.OriginalCircleSA(epoch=1000, pop_size=50, c_factor=0.8)
>>> 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] Qais, M. H., Hasanien, H. M., Turky, R. A., Alghuwainem, S., Tostado-Véliz, M., & Jurado, F. (2022).
Circle Search Algorithm: A Geometry-Based Metaheuristic Optimization Algorithm. Mathematics, 10(10), 1626.
"""
def __init__(self, epoch=10000, pop_size=100, c_factor=0.8, **kwargs):
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.c_factor = self.validator.check_float("c_factor", c_factor, (0, 1.0))
self.set_parameters(["epoch", "pop_size", "c_factor"])
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
"""
a = np.pi - np.pi * (epoch/self.epoch)**2 # Eq. 8
p = 1 - 0.9 * (epoch / self.epoch) ** 0.5
threshold = self.c_factor * self.epoch
pop_new = []
for idx in range(0, self.pop_size):
w = a * self.generator.random() - a
if epoch > threshold:
x_new = self.g_best.solution + (self.g_best.solution - self.pop[idx].solution) * np.tan(w * self.generator.random())
else:
x_new = self.g_best.solution - (self.g_best.solution - self.pop[idx].solution) * np.tan(w * p)
pos_new = self.correct_solution(x_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)
self.pop = self.update_target_for_population(pop_new)