#!/usr/bin/env python
# Created by "Thieu" at 00:08, 27/10/2022 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import numpy as np
from mealpy.optimizer import Optimizer
[docs]class OriginalMGO(Optimizer):
"""
The original version of: Mountain Gazelle Optimizer (MGO)
Links:
1. https://www.sciencedirect.com/science/article/abs/pii/S0965997822001831
2. https://www.mathworks.com/matlabcentral/fileexchange/118680-mountain-gazelle-optimizer
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, MGO
>>>
>>> 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 = MGO.OriginalMGO(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] Abdollahzadeh, B., Gharehchopogh, F. S., Khodadadi, N., & Mirjalili, S. (2022). Mountain gazelle optimizer: a new
nature-inspired metaheuristic algorithm for global optimization problems. Advances in Engineering Software, 174, 103282.
"""
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 = True
[docs] def coefficient_vector__(self, n_dims, epoch, max_epoch):
a2 = -1. + epoch * (-1. / max_epoch)
u = self.generator.standard_normal(n_dims)
v = self.generator.standard_normal(n_dims)
cofi = np.zeros((4, n_dims))
cofi[0, :] = self.generator.random(n_dims)
cofi[1, :] = (a2 + 1) + self.generator.random()
cofi[2, :] = a2 * self.generator.standard_normal(n_dims)
cofi[3, :] = u * np.power(v, 2) * np.cos((self.generator.random() * 2) * u)
return cofi
[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):
idxs_rand = self.generator.permutation(self.pop_size)[:int(np.ceil(self.pop_size/3))]
pos_list = np.array([ self.pop[mm].solution for mm in idxs_rand ])
idx_rand = self.generator.integers(int(np.ceil(self.pop_size / 3)), self.pop_size)
M = self.pop[idx_rand].solution * np.floor(self.generator.normal()) + np.mean(pos_list, axis=0) * np.ceil(self.generator.normal())
# Calculate the vector of coefficients
cofi = self.coefficient_vector__(self.problem.n_dims, epoch+1, self.epoch)
A = self.generator.standard_normal(self.problem.n_dims) * np.exp(2 - (epoch+1) * (2. / self.epoch))
D = (np.abs(self.pop[idx].solution) + np.abs(self.g_best.solution))*(2 * self.generator.random() - 1)
# Update the location
x2 = self.g_best.solution - np.abs((self.generator.integers(1, 3)*M - self.generator.integers(1, 3)*self.pop[idx].solution) * A) * cofi[self.generator.integers(0, 4), :]
x3 = M + cofi[self.generator.integers(0, 4), :] + (self.generator.integers(1, 3)*self.g_best.solution - self.generator.integers(1, 3)*self.pop[self.generator.integers(self.pop_size)].solution)*cofi[self.generator.integers(0, 4), :]
x4 = self.pop[idx].solution - D + (self.generator.integers(1, 3)*self.g_best.solution - self.generator.integers(1, 3)*M) * cofi[self.generator.integers(0, 4), :]
x1 = self.problem.generate_solution()
x1 = self.correct_solution(x1)
x2 = self.correct_solution(x2)
x3 = self.correct_solution(x3)
x4 = self.correct_solution(x4)
agent1 = self.generate_empty_agent(x1)
agent2 = self.generate_empty_agent(x2)
agent3 = self.generate_empty_agent(x3)
agent4 = self.generate_empty_agent(x4)
pop_new += [agent1, agent2, agent3, agent4]
if self.mode not in self.AVAILABLE_MODES:
for jdx in range(-4, 0):
pop_new[jdx].target = self.get_target(pop_new[jdx].solution)
if self.mode in self.AVAILABLE_MODES:
pop_new = self.update_target_for_population(pop_new)
self.pop = self.get_sorted_and_trimmed_population(self.pop + pop_new, self.pop_size, self.problem.minmax)