#!/usr/bin/env python
# Created by "Thieu" at 18:41, 08/04/2020 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import numpy as np
from mealpy.optimizer import Optimizer
[docs]class OriginalEHO(Optimizer):
"""
The original version of: Elephant Herding Optimization (EHO)
Links:
1. https://doi.org/10.1109/ISCBI.2015.8
Hyper-parameters should fine-tune in approximate range to get faster convergence toward the global optimum:
+ alpha (float): [0.3, 0.8], a factor that determines the influence of the best in each clan, default=0.5
+ beta (float): [0.3, 0.8], a factor that determines the influence of the x_center, default=0.5
+ n_clans (int): [3, 10], the number of clans, default=5
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, EHO
>>>
>>> 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 = EHO.OriginalEHO(epoch=1000, pop_size=50, alpha = 0.5, beta = 0.5, n_clans = 5)
>>> 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] Wang, G.G., Deb, S. and Coelho, L.D.S., 2015, December. Elephant herding optimization.
In 2015 3rd international symposium on computational and business intelligence (ISCBI) (pp. 1-5). IEEE.
"""
def __init__(self, epoch: int = 10000, pop_size: int = 100, alpha: float = 0.5, beta: float = 0.5, n_clans: int = 5, **kwargs: object) -> None:
"""
Args:
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size, default = 100
alpha (float): a factor that determines the influence of the best in each clan, default=0.5
beta (float): a factor that determines the influence of the x_center, default=0.5
n_clans (int): the number of clans, default=5
"""
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.alpha = self.validator.check_float("alpha", alpha, (0, 3.0))
self.beta = self.validator.check_float("beta", beta, (0, 1.0))
self.n_clans = self.validator.check_int("n_clans", n_clans, [2, int(self.pop_size/5)])
self.set_parameters(["epoch", "pop_size", "alpha", "beta", "n_clans"])
self.n_individuals = int(self.pop_size / self.n_clans)
self.sort_flag = False
[docs] def initialization(self):
if self.pop is None:
self.pop = self.generate_population(self.pop_size)
self.pop_group = self.generate_group_population(self.pop, self.n_clans, self.n_individuals)
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
# Clan updating operator
pop_new = []
for idx in range(0, self.pop_size):
clan_idx = int(idx / self.n_individuals)
pos_clan_idx = int(idx % self.n_individuals)
if pos_clan_idx == 0: # The best in clan, because all clans are sorted based on fitness
center = np.mean(np.array([agent.solution for agent in self.pop_group[clan_idx]]), axis=0)
pos_new = self.beta * center
else:
pos_new = self.pop_group[clan_idx][pos_clan_idx].solution + self.alpha * np.random.uniform() * \
(self.pop_group[clan_idx][0].solution - self.pop_group[clan_idx][pos_clan_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)
self.pop_group = self.generate_group_population(self.pop, self.n_clans, self.n_individuals)
# Separating operator
for idx in range(0, self.n_clans):
self.pop_group[idx] = self.get_sorted_population(self.pop_group[idx], self.problem.minmax)
self.pop_group[idx][-1] = self.generate_agent()
self.pop = [agent for pack in self.pop_group for agent in pack]