#!/usr/bin/env python
# Created by "Thieu" at 17:36, 21/05/2022 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import numpy as np
from mealpy.optimizer import Optimizer
[docs]class OriginalSCSO(Optimizer):
"""
The original version of: Sand Cat Swarm Optimization (SCSO)
Links:
1. https://link.springer.com/article/10.1007/s00366-022-01604-x
2. https://www.mathworks.com/matlabcentral/fileexchange/110185-sand-cat-swarm-optimization
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, SCSO
>>>
>>> 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 = SCSO.OriginalSCSO(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] Seyyedabbasi, A., & Kiani, F. (2022). Sand Cat swarm optimization: a nature-inspired algorithm to
solve global optimization problems. Engineering with Computers, 1-25.
"""
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 initialize_variables(self):
self.ss = 2 # maximum Sensitivity range
self.pp = np.arange(1, 361)
[docs] def get_index_roulette_wheel_selection__(self, p):
p = p / np.sum(p)
c = np.cumsum(p)
return np.argwhere(self.generator.random() < c)[0][0]
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
guides_r = self.ss - (self.ss * epoch / self.epoch)
pop_new = []
for idx in range(0, self.pop_size):
r = self.generator.random() * guides_r
R = (2*guides_r)*self.generator.random() - guides_r # controls to transition phases
pos_new = self.pop[idx].solution.copy()
for jdx in range(0, self.problem.n_dims):
teta = self.get_index_roulette_wheel_selection__(self.pp)
if -1 <= R <= 1:
rand_pos = np.abs(self.generator.random() * self.g_best.solution[jdx] - self.pop[idx].solution[jdx])
pos_new[jdx] = self.g_best.solution[jdx] - r * rand_pos * np.cos(teta)
else:
cp = int(self.generator.random() * self.pop_size)
pos_new[jdx] = r * (self.pop[cp].solution[jdx] - self.generator.random() * self.pop[idx].solution[jdx])
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)