# !/usr/bin/env python
# Created by "Thieu" at 11:17, 18/03/2020 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import numpy as np
from copy import deepcopy
from mealpy.optimizer import Optimizer
[docs]class BaseSSDO(Optimizer):
"""
The original version of: Social Ski-Driver Optimization (SSDO)
Links:
1. https://doi.org/10.1007/s00521-019-04159-z
2. https://www.mathworks.com/matlabcentral/fileexchange/71210-social-ski-driver-ssd-optimization-algorithm-2019
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy.human_based.SSDO import BaseSSDO
>>>
>>> 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 = BaseSSDO(problem_dict1, epoch, pop_size)
>>> best_position, best_fitness = model.solve()
>>> print(f"Solution: {best_position}, Fitness: {best_fitness}")
References
~~~~~~~~~~
[1] Tharwat, A. and Gabel, T., 2020. Parameters optimization of support vector machines for imbalanced
data using social ski driver algorithm. Neural Computing and Applications, 32(11), pp.6925-6938.
"""
ID_VEL = 2
ID_LOC = 3
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 = False
self.epoch = epoch
self.pop_size = pop_size
[docs] def create_solution(self):
"""
To get the position, fitness wrapper, target and obj list
+ A[self.ID_POS] --> Return: position
+ A[self.ID_TAR] --> Return: [target, [obj1, obj2, ...]]
+ A[self.ID_TAR][self.ID_FIT] --> Return: target
+ A[self.ID_TAR][self.ID_OBJ] --> Return: [obj1, obj2, ...]
Returns:
list: wrapper of solution with format [position, [target, [obj1, obj2, ...]], velocity, best_local_position]
"""
position = np.random.uniform(self.problem.lb, self.problem.ub)
position = self.amend_position(position)
fitness = self.get_fitness_position(position=position)
velocity = np.random.uniform(self.problem.lb, self.problem.ub)
pos_local = deepcopy(position)
return [position, fitness, velocity, pos_local]
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
c = 2 - epoch * (2.0 / self.epoch) # a decreases linearly from 2 to 0
## Calculate the mean of the best three solutions in each dimension. Eq 9
_, pop_best3, _ = self.get_special_solutions(self.pop, best=3)
pos_mean = np.mean(np.array([item[self.ID_POS] for item in pop_best3]))
pop_new = deepcopy(self.pop)
# Updating velocity vectors
for i in range(0, self.pop_size):
r1 = np.random.uniform() # r1, r2 is a random number in [0,1]
r2 = np.random.uniform()
if r2 <= 0.5: ## Use Sine function to move
vel_new = c * np.sin(r1) * (self.pop[i][self.ID_LOC] - self.pop[i][self.ID_POS]) + np.sin(r1) * (pos_mean - self.pop[i][self.ID_POS])
else: ## Use Cosine function to move
vel_new = c * np.cos(r1) * (self.pop[i][self.ID_LOC] - self.pop[i][self.ID_POS]) + np.cos(r1) * (pos_mean - self.pop[i][self.ID_POS])
pop_new[i][self.ID_VEL] = vel_new
## Reproduction
for idx in range(0, self.pop_size):
pos_new = pop_new[idx][self.ID_POS] + pop_new[idx][self.ID_VEL]
pos_new = self.amend_position(pos_new)
pop_new[idx][self.ID_POS] = pos_new
pop_new = self.update_fitness_population(pop_new)
self.pop = self.greedy_selection_population(self.pop, pop_new)