# !/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 BaseMRFO(Optimizer):
"""
The original version of: Manta Ray Foraging Optimization (MRFO)
Links:
1. https://doi.org/10.1016/j.engappai.2019.103300
Hyper-parameters should fine tuned in approximate range to get faster convergen toward the global optimum:
+ somersault_range (float): [1.5, 3], somersault factor that decides the somersault range of manta rays, default=2
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy.swarm_based.MRFO import BaseMRFO
>>>
>>> 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
>>> sample_count = 50
>>> inten_factor = 0.5
>>> zeta = 1.0
>>> model = BaseMRFO(problem_dict1, epoch, pop_size, sample_count, inten_factor, zeta)
>>> best_position, best_fitness = model.solve()
>>> print(f"Solution: {best_position}, Fitness: {best_fitness}")
References
~~~~~~~~~~
[1] Zhao, W., Zhang, Z. and Wang, L., 2020. Manta ray foraging optimization: An effective bio-inspired
optimizer for engineering applications. Engineering Applications of Artificial Intelligence, 87, p.103300.
"""
def __init__(self, problem, epoch=10000, pop_size=100, somersault_range=2.0, **kwargs):
"""
Args:
problem (dict): The problem dictionary
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size, default = 100
somersault_range (float): somersault factor that decides the somersault range of manta rays, default=2
"""
super().__init__(problem, kwargs)
self.nfe_per_epoch = 2 * pop_size
self.sort_flag = False
self.epoch = epoch
self.pop_size = pop_size
self.somersault_range = somersault_range
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
pop_new = []
for idx in range(0, self.pop_size):
# Cyclone foraging (Eq. 5, 6, 7)
if np.random.rand() < 0.5:
r1 = np.random.uniform()
beta = 2 * np.exp(r1 * (self.epoch - epoch) / self.epoch) * np.sin(2 * np.pi * r1)
if (epoch + 1) / self.epoch < np.random.rand():
x_rand = np.random.uniform(self.problem.lb, self.problem.ub)
if idx == 0:
x_t1 = x_rand + np.random.uniform() * (x_rand - self.pop[idx][self.ID_POS]) + \
beta * (x_rand - self.pop[idx][self.ID_POS])
else:
x_t1 = x_rand + np.random.uniform() * (self.pop[idx - 1][self.ID_POS] - self.pop[idx][self.ID_POS]) + \
beta * (x_rand - self.pop[idx][self.ID_POS])
else:
if idx == 0:
x_t1 = self.g_best[self.ID_POS] + np.random.uniform() * (self.g_best[self.ID_POS] - self.pop[idx][self.ID_POS]) + \
beta * (self.g_best[self.ID_POS] - self.pop[idx][self.ID_POS])
else:
x_t1 = self.g_best[self.ID_POS] + np.random.uniform() * (self.pop[idx - 1][self.ID_POS] - self.pop[idx][self.ID_POS]) + \
beta * (self.g_best[self.ID_POS] - self.pop[idx][self.ID_POS])
# Chain foraging (Eq. 1,2)
else:
r = np.random.uniform()
alpha = 2 * r * np.sqrt(np.abs(np.log(r)))
if idx == 0:
x_t1 = self.pop[idx][self.ID_POS] + r * (self.g_best[self.ID_POS] - self.pop[idx][self.ID_POS]) + \
alpha * (self.g_best[self.ID_POS] - self.pop[idx][self.ID_POS])
else:
x_t1 = self.pop[idx][self.ID_POS] + r * (self.pop[idx - 1][self.ID_POS] - self.pop[idx][self.ID_POS]) + \
alpha * (self.g_best[self.ID_POS] - self.pop[idx][self.ID_POS])
pos_new = self.amend_position(x_t1)
pop_new.append([pos_new, None])
pop_new = self.update_fitness_population(pop_new)
pop_new = self.greedy_selection_population(self.pop, pop_new)
_, g_best = self.update_global_best_solution(pop_new, save=False)
pop_child = []
for idx in range(0, self.pop_size):
# Somersault foraging (Eq. 8)
x_t1 = pop_new[idx][self.ID_POS] + self.somersault_range * \
(np.random.uniform() * g_best[self.ID_POS] - np.random.uniform() * pop_new[idx][self.ID_POS])
pos_new = self.amend_position(x_t1)
pop_child.append([pos_new, None])
pop_child = self.update_fitness_population(pop_child)
self.pop = self.greedy_selection_population(pop_new, pop_child)