#!/usr/bin/env python
# Created by "Thieu" at 14:20, 15/10/2022 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
from mealpy.optimizer import Optimizer
[docs]class OriginalSOS(Optimizer):
"""
The original version: Symbiotic Organisms Search (SOS)
Links:
1. https://doi.org/10.1016/j.compstruc.2014.03.007
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, SOS
>>>
>>> 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 = SOS.OriginalSOS(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] Cheng, M. Y., & Prayogo, D. (2014). Symbiotic organisms search: a new metaheuristic
optimization algorithm. Computers & Structures, 139, 98-112.
"""
def __init__(self, epoch=10000, pop_size=100, **kwargs):
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.is_parallelizable = False
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
"""
for idx in range(0, self.pop_size):
## Mutualism Phase
jdx = self.generator.choice(list(set(range(0, self.pop_size)) - {idx}))
mutual_vector = (self.pop[idx].solution + self.pop[jdx].solution) / 2
bf1, bf2 = self.generator.integers(1, 3, 2)
xi_new = self.pop[idx].solution + self.generator.random() * (self.g_best.solution - bf1 * mutual_vector)
xj_new = self.pop[jdx].solution + self.generator.random() * (self.g_best.solution - bf2 * mutual_vector)
xi_new = self.correct_solution(xi_new)
xj_new = self.correct_solution(xj_new)
xi_target = self.get_target(xi_new)
xj_target = self.get_target(xj_new)
if self.compare_target(xi_target, self.pop[idx].target, self.problem.minmax):
self.pop[idx].update(solution=xi_new, target=xi_target)
if self.compare_target(xj_target, self.pop[jdx].target, self.problem.minmax):
self.pop[jdx].update(solution=xj_new, target=xj_target)
## Commensalism phase
jdx = self.generator.choice(list(set(range(0, self.pop_size)) - {idx}))
xi_new = self.pop[idx].solution + self.generator.uniform(-1, 1) * (self.g_best.solution - self.pop[jdx].solution)
xi_new = self.correct_solution(xi_new)
xi_target = self.get_target(xi_new)
if self.compare_target(xi_target, self.pop[idx].target, self.problem.minmax):
self.pop[idx].update(solution=xi_new, target=xi_target)
## Parasitism phase
jdx = self.generator.choice(list(set(range(0, self.pop_size)) - {idx}))
temp_idx = self.generator.integers(0, self.problem.n_dims)
xi_new = self.pop[jdx].solution.copy()
xi_new[temp_idx] = self.problem.generate_solution()[temp_idx]
xi_new = self.correct_solution(xi_new)
xi_target = self.get_target(xi_new)
if self.compare_target(xi_target, self.pop[idx].target, self.problem.minmax):
self.pop[idx].update(solution=xi_new, target=xi_target)