#!/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 OriginalFOX(Optimizer):
"""
The original version of: Fox Optimizer (FOX)
Links:
1. https://link.springer.com/article/10.1007/s10489-022-03533-0
2. https://www.mathworks.com/matlabcentral/fileexchange/121592-fox-a-fox-inspired-optimization-algorithm
Notes (parameters):
1. c1 (float): the probability of jumping (c1 in the paper), default = 0.18
2. c2 (float): the probability of jumping (c2 in the paper), default = 0.82
Notes:
1. The equation used to calculate the distance_S_travel value in the Matlab code seems to be lacking in meaning.
2. The if-else conditions used with p > 0.18 seem to lack a clear justification. The authors seem to have simply chosen the best value based on their experiments without explaining the rationale behind it.
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, FOX
>>>
>>> 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 = FOX.OriginalFOX(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] Mohammed, H., & Rashid, T. (2023). FOX: a FOX-inspired optimization algorithm. Applied Intelligence, 53(1), 1030-1050.
"""
def __init__(self, epoch: int = 10000, pop_size: int = 100, c1: float = 0.18, c2: float = 0.82, **kwargs: object) -> None:
"""
Args:
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size, default = 100
c1 (float): the probability of jumping (c1 in the paper), default = 0.18
c2 (float): the probability of jumping (c2 in the paper), default = 0.82
"""
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.c1 = self.validator.check_float("c1", c1, (-100., 100.)) # c1 in the paper
self.c2 = self.validator.check_float("c2", c2, (-100., 100.)) # c2 in the paper
self.set_parameters(["epoch", "pop_size"])
self.sort_flag = False
[docs] def initialize_variables(self):
self.mint = 10000000
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
aa = 2 * (1 - (1.0 / self.epoch))
pop_new = []
for idx in range(0, self.pop_size):
if self.generator.random() >= 0.5:
t1 = self.generator.random(self.problem.n_dims)
sps = self.g_best.solution / t1
dis = 0.5 * sps * t1
tt = np.mean(t1)
t = tt / 2
jump = 0.5 * 9.81 * t ** 2
if self.generator.random() > 0.18:
pos_new = dis * jump * self.c1
else:
pos_new = dis * jump * self.c2
if self.mint > tt:
self.mint = tt
else:
pos_new = self.g_best.solution + self.generator.standard_normal(self.problem.n_dims) * (self.mint * aa)
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] = agent
if self.mode in self.AVAILABLE_MODES:
self.pop = self.update_target_for_population(pop_new)