#!/usr/bin/env python
# Created by "Thieu" at 14:52, 17/03/2020 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import numpy as np
from mealpy.optimizer import Optimizer
[docs]class OriginalBES(Optimizer):
"""
The original version of: Bald Eagle Search (BES)
Links:
1. https://doi.org/10.1007/s10462-019-09732-5
Hyper-parameters should fine-tune in approximate range to get faster convergence toward the global optimum:
+ a_factor (int): default: 10, determining the corner between point search in the central point, in [5, 10]
+ R_factor (float): default: 1.5, determining the number of search cycles, in [0.5, 2]
+ alpha (float): default: 2, parameter for controlling the changes in position, in [1.5, 2]
+ c1 (float): default: 2, in [1, 2]
+ c2 (float): c1 and c2 increase the movement intensity of bald eagles towards the best and centre points
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, BES
>>>
>>> def objective_function(solution):
>>> return np.sum(solution**2)
>>>
>>> problem_dict = {
>>> "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
>>> "obj_func": objective_function,
>>> "minmax": "min",
>>> }
>>>
>>> model = BES.OriginalBES(epoch=1000, pop_size=50, a_factor = 10, R_factor = 1.5, alpha = 2.0, c1 = 2.0, c2 = 2.0)
>>> 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] Alsattar, H.A., Zaidan, A.A. and Zaidan, B.B., 2020. Novel meta-heuristic bald eagle
search optimisation algorithm. Artificial Intelligence Review, 53(3), pp.2237-2264.
"""
def __init__(self, epoch: int = 10000, pop_size: int = 100, a_factor: int = 10, R_factor: float = 1.5,
alpha: float = 2.0, c1: float = 2.0, c2: float = 2.0, **kwargs: object) -> None:
"""
Args:
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size, default = 100
a_factor (int): default: 10, determining the corner between point search in the central point, in [5, 10]
R_factor (float): default: 1.5, determining the number of search cycles, in [0.5, 2]
alpha (float): default: 2, parameter for controlling the changes in position, in [1.5, 2]
c1 (float): default: 2, in [1, 2]
c2 (float): c1 and c2 increase the movement intensity of bald eagles towards the best and centre points
"""
super().__init__(**kwargs)
self.epoch = self.validator.check_int("epoch", epoch, [1, 100000])
self.pop_size = self.validator.check_int("pop_size", pop_size, [10, 10000])
self.a_factor = self.validator.check_int("a_factor", a_factor, [2, 20])
self.R_factor = self.validator.check_float("R_factor", R_factor, [0.1, 3.0])
self.alpha = self.validator.check_float("alpha", alpha, [0.5, 3.0])
self.c1 = self.validator.check_float("c1", c1, (0, 4.0))
self.c2 = self.validator.check_float("c2", c2, (0, 4.0))
self.set_parameters(["epoch", "pop_size", "a_factor", "R_factor", "alpha", "c1", "c2"])
self.sort_flag = False
[docs] def create_x_y_x1_y1__(self):
""" Using numpy vector for faster computational time """
## Eq. 2
phi = self.a_factor * np.pi * self.generator.uniform(0, 1, self.pop_size)
r = phi + self.R_factor * self.generator.uniform(0, 1, self.pop_size)
xr, yr = r * np.sin(phi), r * np.cos(phi)
## Eq. 3
r1 = phi1 = self.a_factor * np.pi * self.generator.uniform(0, 1, self.pop_size)
xr1, yr1 = r1 * np.sinh(phi1), r1 * np.cosh(phi1)
x_list = xr / np.max(xr)
y_list = yr / np.max(yr)
x1_list = xr1 / np.max(xr1)
y1_list = yr1 / np.max(yr1)
return x_list, y_list, x1_list, y1_list
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
## 0. Pre-definded
x_list, y_list, x1_list, y1_list = self.create_x_y_x1_y1__()
# Three parts: selecting the search space, searching within the selected search space and swooping.
## 1. Select space
pos_list = np.array([agent.solution for agent in self.pop])
pos_mean = np.mean(pos_list, axis=0)
pop_new = []
for idx in range(0, self.pop_size):
pos_new = self.g_best.solution + self.alpha * self.generator.uniform() * (pos_mean - 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(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)
## 2. Search in space
pos_list = np.array([agent.solution for agent in self.pop])
pos_mean = np.mean(pos_list, axis=0)
pop_child = []
for idx in range(0, self.pop_size):
idx_rand = self.generator.choice(list(set(range(0, self.pop_size)) - {idx}))
pos_new = self.pop[idx].solution + y_list[idx] * (self.pop[idx].solution - self.pop[idx_rand].solution) + \
x_list[idx] * (self.pop[idx].solution - pos_mean)
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)
## 3. Swoop
pos_list = np.array([agent.solution for agent in self.pop])
pos_mean = np.mean(pos_list, axis=0)
pop_new = []
for idx in range(0, self.pop_size):
pos_new = self.generator.uniform() * self.g_best.solution + x1_list[idx] * (self.pop[idx].solution - self.c1 * pos_mean) \
+ y1_list[idx] * (self.pop[idx].solution - self.c2 * self.g_best.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(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)