# !/usr/bin/env python
# Created by "Thieu" at 11:38, 02/03/2021 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import numpy as np
from mealpy.optimizer import Optimizer
[docs]class BaseSSO(Optimizer):
"""
The original version of: Salp Swarm Optimization (SSO)
Links:
1. https://doi.org/10.1016/j.advengsoft.2017.07.002
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy.swarm_based.SSO import BaseSSO
>>>
>>> def fitness_function(solution):
>>> return np.sum(solution**2)
>>>
>>> problem_dict1 = {
>>> "fit_func": fitness_function,
>>> "lb": [-10, -15, -4, -2, -8],
>>> "ub": [10, 15, 12, 8, 20],
>>> "minmax": "min",
>>> "verbose": True,
>>> }
>>>
>>> epoch = 1000
>>> pop_size = 50
>>> model = BaseSSO(problem_dict1, epoch, pop_size)
>>> best_position, best_fitness = model.solve()
>>> print(f"Solution: {best_position}, Fitness: {best_fitness}")
References
~~~~~~~~~~
[1] Mirjalili, S., Gandomi, A.H., Mirjalili, S.Z., Saremi, S., Faris, H. and Mirjalili, S.M., 2017.
Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in
Engineering Software, 114, pp.163-191.
"""
def __init__(self, problem, epoch=10000, pop_size=100, **kwargs):
"""
Args:
problem (dict): The problem dictionary
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size, default = 100
"""
super().__init__(problem, kwargs)
self.nfe_per_epoch = pop_size
self.sort_flag = True
self.epoch = epoch
self.pop_size = pop_size
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
## Eq. (3.2) in the paper
c1 = 2 * np.exp(-((4 * (epoch + 1) / self.epoch) ** 2))
pop_new = []
for idx in range(0, self.pop_size):
if idx < self.pop_size / 2:
c2_list = np.random.random(self.problem.n_dims)
c3_list = np.random.random(self.problem.n_dims)
pos_new_1 = self.g_best[self.ID_POS] + c1 * ((self.problem.ub - self.problem.lb) * c2_list + self.problem.lb)
pos_new_2 = self.g_best[self.ID_POS] - c1 * ((self.problem.ub - self.problem.lb) * c2_list + self.problem.lb)
pos_new = np.where(c3_list < 0.5, pos_new_1, pos_new_2)
else:
# Eq. (3.4) in the paper
pos_new = (self.pop[idx][self.ID_POS] + self.pop[idx - 1][self.ID_POS]) / 2
# Check if salps go out of the search space and bring it back then re-calculate its fitness value
pos_new = self.amend_position(pos_new)
pop_new.append([pos_new, None])
pop_new = self.update_fitness_population(pop_new)
self.pop = self.greedy_selection_population(self.pop, pop_new)