#!/usr/bin/env python
# Created by "Thieu" at 15:37, 19/03/2021 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import numpy as np
from mealpy.optimizer import Optimizer
from mealpy.utils.agent import Agent
[docs]class OriginalHGS(Optimizer):
"""
The original version of: Hunger Games Search (HGS)
Links:
https://aliasgharheidari.com/HGS.html
Hyper-parameters should fine-tune in approximate range to get faster convergence toward the global optimum:
+ PUP (float): [0.01, 0.2], The probability of updating position (L in the paper), default = 0.08
+ LH (float): [1000, 20000], Largest hunger / threshold, default = 10000
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, HGS
>>>
>>> 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 = HGS.OriginalHGS(epoch=1000, pop_size=50, PUP = 0.08, LH = 10000)
>>> 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] Yang, Y., Chen, H., Heidari, A.A. and Gandomi, A.H., 2021. Hunger games search: Visions, conception, implementation,
deep analysis, perspectives, and towards performance shifts. Expert Systems with Applications, 177, p.114864.
"""
ID_HUN = 2 # ID for Hunger value
def __init__(self, epoch: int = 10000, pop_size: int = 100, PUP: float = 0.08, LH: float = 10000, **kwargs: object) -> None:
"""
Args:
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size, default = 100
PUP (float): The probability of updating position (L in the paper), default = 0.08
LH (float): Largest hunger / threshold, default = 10000
"""
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.PUP = self.validator.check_float("PUP", PUP, (0, 1.0))
self.LH = self.validator.check_float("LH", LH, [1, 20000])
self.set_parameters(["epoch", "pop_size", "PUP", "LH"])
self.sort_flag = False
[docs] def generate_empty_agent(self, solution: np.ndarray = None) -> Agent:
if solution is None:
solution = self.problem.generate_solution(encoded=True)
hunger = 1.0
return Agent(solution=solution, hunger=hunger)
[docs] def sech__(self, x):
if np.abs(x) > 50:
return 0.5
return 2 / (np.exp(x) + np.exp(-x))
[docs] def update_hunger_value__(self, pop=None, g_best=None, g_worst=None):
# min_index = pop.index(min(pop, key=lambda x: x.target.fitness))
# Eq (2.8) and (2.9)
for idx in range(0, self.pop_size):
r = self.generator.random()
# space: since we pass lower bound and upper bound as list. Better take the np.mean of them.
space = np.mean(self.problem.ub - self.problem.lb)
H = (pop[idx].target.fitness - g_best.target.fitness) / \
(g_worst.target.fitness - g_best.target.fitness + self.EPSILON) * r * 2 * space
if H < self.LH:
H = self.LH * (1 + r)
pop[idx].hunger += H
if g_best.target.fitness == pop[idx].target.fitness:
pop[idx].hunger = 0
return pop
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
## Eq. (2.2)
### Find the current best and current worst
_, (g_best, ), (g_worst, ) = self.get_special_agents(self.pop, n_best=1, n_worst=1, minmax=self.problem.minmax)
pop = self.update_hunger_value__(self.pop, g_best, g_worst)
## Eq. (2.4)
shrink = 2 * (1 - (epoch + 1) / self.epoch)
total_hunger = np.sum([pop[idx].hunger for idx in range(0, self.pop_size)])
pop_new = []
for idx in range(0, self.pop_size):
agent = self.pop[idx].copy()
#### Variation control
E = self.sech__(self.pop[idx].target.fitness - g_best.target.fitness)
# R is a ranging controller added to limit the range of activity, in which the range of R is gradually reduced to 0
R = 2 * shrink * self.generator.random() - shrink # Eq. (2.3)
## Calculate the hungry weight of each position
if self.generator.random() < self.PUP:
W1 = self.pop[idx].hunger * self.pop_size / (total_hunger + self.EPSILON) * self.generator.random()
else:
W1 = 1
W2 = (1 - np.exp(-np.abs(self.pop[idx].hunger - total_hunger))) * self.generator.random() * 2
### Udpate position of individual Eq. (2.1)
r1 = self.generator.random()
r2 = self.generator.random()
if r1 < self.PUP:
pos_new = self.pop[idx].solution * (1 + self.generator.normal(0, 1))
else:
if r2 > E:
pos_new = W1 * g_best.solution + R * W2 * np.abs(g_best.solution - self.pop[idx].solution)
else:
pos_new = W1 * g_best.solution - R * W2 * np.abs(g_best.solution - self.pop[idx].solution)
pos_new = self.correct_solution(pos_new)
agent.solution = 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(self.pop[idx], agent, 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)