#!/usr/bin/env python
# Created by "Thieu" at 22:24, 02/03/2022 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import numpy as np
from mealpy.optimizer import Optimizer
[docs]class OriginalCGO(Optimizer):
"""
The original version of: Chaos Game Optimization (CGO)
Links:
1. https://doi.org/10.1007/s10462-020-09867-w
Notes:
+ 4th seed is mutation process, but it is not clear mutation on multiple variables or 1 variable
+ There is no usage of the variable alpha 4th in the paper
+ The replacement of the worst solutions by generated seed are not clear (Lots of grammar errors in this section)
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, CGO
>>>
>>> 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 = CGO.OriginalCGO(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] Talatahari, S. and Azizi, M., 2021. Chaos Game Optimization: a novel metaheuristic algorithm.
Artificial Intelligence Review, 54(2), pp.917-1004.
"""
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.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
"""
pop_new = []
for idx in range(0, self.pop_size):
s1, s2, s3 = self.generator.choice(range(0, self.pop_size), 3, replace=False)
MG = (self.pop[s1].solution + self.pop[s2].solution + self.pop[s3].solution) / 3
## Calculating alpha based on Eq. 7
alpha1 = self.generator.random()
alpha2 = 2 * self.generator.random()
alpha3 = 1 + self.generator.random() * self.generator.random()
esp = self.generator.random()
# There is no usage of this variable in the paper
alpha4 = esp + esp * self.generator.random()
beta = self.generator.integers(0, 2, 3)
gama = self.generator.integers(0, 2, 3)
## The seed4 is mutation process, but not sure k is multiple variables or 1 variable.
## In the text said, multiple variables, but the defination of k is 1 variable. So confused
k = self.generator.integers(0, self.problem.n_dims)
k_idx = self.generator.choice(range(0, self.problem.n_dims), k, replace=False)
seed1 = self.pop[idx].solution + alpha1 * (beta[0] * self.g_best.solution - gama[0] * MG) # Eq. 3
seed2 = self.g_best.solution + alpha2 * (beta[1] * self.pop[idx].solution - gama[1] * MG) # Eq. 4
seed3 = MG + alpha3 * (beta[2] * self.pop[idx].solution - gama[2] * self.g_best.solution) # Eq. 5
seed4 = self.pop[idx].solution.copy().astype(float)
seed4[k_idx] += self.generator.uniform(0, 1, k)
# Check if solutions go outside the search space and bring them back
seed1 = self.correct_solution(seed1)
seed2 = self.correct_solution(seed2)
seed3 = self.correct_solution(seed3)
seed4 = self.correct_solution(seed4)
agent1 = self.generate_agent(seed1)
agent2 = self.generate_agent(seed2)
agent3 = self.generate_agent(seed3)
agent4 = self.generate_agent(seed4)
## Lots of grammar errors in this section, so confused to understand which strategy they are using
best_seed = self.get_best_agent([agent1, agent2, agent3, agent4], self.problem.minmax)
self.pop[idx] = self.get_better_agent(best_seed, self.pop[idx], self.problem.minmax)