#!/usr/bin/env python
# Created by "Thieu" at 17:52, 21/05/2022 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import numpy as np
from mealpy.optimizer import Optimizer
[docs]class OriginalTSO(Optimizer):
"""
The original version of: Tuna Swarm Optimization (TSO)
Notes:
1. Two variables that authors consider it as a constants (aa = 0.7 and zz = 0.05)
2. https://www.hindawi.com/journals/cin/2021/9210050/
3. https://www.mathworks.com/matlabcentral/fileexchange/101734-tuna-swarm-optimization
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, TSO
>>>
>>> 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 = TSO.OriginalTSO(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] Xie, L., Han, T., Zhou, H., Zhang, Z. R., Han, B., & Tang, A. (2021). Tuna swarm optimization: a novel swarm-based
metaheuristic algorithm for global optimization. Computational intelligence and Neuroscience, 2021.
"""
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 = True
[docs] def initialize_variables(self):
self.aa = 0.7
self.zz = 0.05
[docs] def get_new_local_pos__(self, C, a1, a2, t, epoch):
if self.generator.random() < self.zz:
local_pos = self.problem.generate_solution()
else:
if self.generator.random() < 0.5:
r1 = self.generator.random()
beta = np.exp(r1 * np.exp(3*np.cos(np.pi*((self.epoch - epoch) / self.epoch)))) * np.cos(2*np.pi*r1)
if self.generator.random() < C:
local_pos = a1*(self.g_best.solution + beta * np.abs(self.g_best.solution - self.pop[0].solution)) + a2 * self.pop[0].solution # Eq (8.3)
else:
rand_pos = self.problem.generate_solution()
local_pos = a1 * (rand_pos + beta*np.abs(rand_pos - self.pop[0].solution)) + a2 * self.pop[0].solution # Eq (8.1)
else:
tf = self.generator.choice([-1, 1])
if self.generator.random() < 0.5:
local_pos = tf * t**2 * self.pop[0].solution # Eq 9.2
else:
local_pos = self.g_best.solution + self.generator.random(self.problem.n_dims) * (self.g_best.solution - self.pop[0].solution) + \
tf * t**2 * (self.g_best.solution - self.pop[0].solution)
return local_pos
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
C = epoch / self.epoch
a1 = self.aa + (1 - self.aa) * C
a2 = (1 - self.aa) - (1 - self.aa) * C
tt = (1 - epoch / self.epoch) ** (epoch / self.epoch)
pop_new = []
for idx in range(0, self.pop_size):
if idx == 0:
pos_new = self.get_new_local_pos__(C, a1, a2, tt, epoch)
else:
if self.generator.random() < self.zz:
pos_new = self.problem.generate_solution()
else:
if self.generator.random() > 0.5:
r1 = self.generator.random()
beta = np.exp(r1 * np.exp(3*np.cos(np.pi * (self.epoch - epoch)/self.epoch))) * np.cos(2*np.pi*r1)
if self.generator.random() < C:
pos_new = a1 * (self.g_best.solution + beta*np.abs(self.g_best.solution - self.pop[idx].solution)) + \
a2 * self.pop[idx-1].solution # Eq. 8.4
else:
rand_pos = self.problem.generate_solution()
pos_new = a1 * (rand_pos + beta*np.abs(rand_pos - self.pop[idx].solution)) + a2 * self.pop[idx-1].solution # Eq 8.2
else:
tf = self.generator.choice([-1, 1])
if self.generator.random() < 0.5:
pos_new = self.g_best.solution + self.generator.random(self.problem.n_dims) * \
(self.g_best.solution - self.pop[idx].solution) + tf * tt**2 * (self.g_best.solution - self.pop[idx].solution) # Eq 9.1
else:
pos_new = tf * tt**2 * self.pop[idx].solution # Eq 9.2
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)
self.pop = self.update_target_for_population(pop_new)