#!/usr/bin/env python
# Created by "Thieu" at 18:09, 13/03/2023 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import numpy as np
from mealpy.optimizer import Optimizer
[docs]class OriginalSHIO(Optimizer):
"""
The original version of: Success History Intelligent Optimizer (SHIO)
Links:
1. https://link.springer.com/article/10.1007/s11227-021-04093-9
2. https://www.mathworks.com/matlabcentral/fileexchange/122157-success-history-intelligent-optimizer-shio
Notes:
1. The algorithm is designed with simplicity and ease of implementation in mind, utilizing basic operators.
2. This algorithm has several limitations and weak when dealing with several problems
3. The algorithm's convergence is slow. The Matlab code has many errors and unnecessary things.
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, SHIO
>>>
>>> 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 = SHIO.OriginalSHIO(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] Fakhouri, H. N., Hamad, F., & Alawamrah, A. (2022). Success history intelligent optimizer. The Journal of Supercomputing, 1-42.
"""
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 = False
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
_, (b1, b2, b3), _ = self.get_special_agents(self.pop, n_best=3, n_worst=1, minmax=self.problem.minmax)
a = 1.5
pop_new = []
for idx in range(0, self.pop_size):
a = a - 0.04
x1 = b1.solution + (a*2*self.generator.random(self.problem.n_dims) - a)*np.abs(self.generator.random(self.problem.n_dims) * b1.solution - self.pop[idx].solution)
x2 = b2.solution + (a*2*self.generator.random(self.problem.n_dims) - a)*np.abs(self.generator.random(self.problem.n_dims) * b2.solution - self.pop[idx].solution)
x3 = b3.solution + (a*2*self.generator.random(self.problem.n_dims) - a)*np.abs(self.generator.random(self.problem.n_dims) * b3.solution - self.pop[idx].solution)
pos_new = (x1 + x2 + x3) / 3
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:
pop_new[-1].target = self.get_target(pos_new)
if self.mode in self.AVAILABLE_MODES:
pop_new = self.update_target_for_population(pop_new)
self.pop = pop_new