# !/usr/bin/env python
# Created by "Thieu" at 12:09, 02/03/2021 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import numpy as np
from mealpy.optimizer import Optimizer
[docs]class OriginalCA(Optimizer):
"""
The original version of: Culture Algorithm (CA)
Links:
1. https://github.com/clever-algorithms/CleverAlgorithms
Hyper-parameters should fine tuned in approximate range to get faster convergen toward the global optimum:
+ accepted_rate (float): [0.1, 0.5], probability of accepted rate, default: 0.15
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy.human_based.CA import OriginalCA
>>>
>>> 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
>>> accepted_rate = 0.15
>>> model = OriginalCA(problem_dict1, epoch, pop_size, accepted_rate)
>>> best_position, best_fitness = model.solve()
>>> print(f"Solution: {best_position}, Fitness: {best_fitness}")
References
~~~~~~~~~~
[1] Chen, B., Zhao, L. and Lu, J.H., 2009, April. Wind power forecast using RBF network and culture algorithm.
In 2009 International Conference on Sustainable Power Generation and Supply (pp. 1-6). IEEE.
"""
def __init__(self, problem, epoch=10000, pop_size=100, accepted_rate=0.15, **kwargs):
"""
Args:
problem (dict): The problem dictionary
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size, default = 100
accepted_rate (float): probability of accepted rate, default: 0.15
"""
super().__init__(problem, kwargs)
self.nfe_per_epoch = pop_size
self.sort_flag = True
self.epoch = epoch
self.pop_size = pop_size
self.accepted_rate = accepted_rate
## Dynamic variables
self.dyn_belief_space = {
"lb": self.problem.lb,
"ub": self.problem.ub,
}
self.dyn_accepted_num = int(self.accepted_rate * self.pop_size)
# update situational knowledge (g_best here is a element inside belief space)
[docs] def create_faithful(self, lb, ub):
position = np.random.uniform(lb, ub)
position = self.amend_position(position)
fitness = self.get_fitness_position(position=position)
return [position, fitness]
[docs] def update_belief_space(self, belief_space, pop_accepted):
pos_list = np.array([solution[self.ID_POS] for solution in pop_accepted])
belief_space["lb"] = np.min(pos_list, axis=0)
belief_space["ub"] = np.max(pos_list, axis=0)
return belief_space
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
# create next generation
pop_child = [self.create_faithful(self.dyn_belief_space["lb"], self.dyn_belief_space["ub"]) for _ in range(0, self.pop_size)]
# select next generation
pop_new = []
pop_full = self.pop + pop_child
size_new = len(pop_full)
for _ in range(0, self.pop_size):
id1, id2 = np.random.choice(list(range(0, size_new)), 2, replace=False)
if self.compare_agent(pop_full[id1], pop_full[id2]):
pop_new.append(pop_full[id1])
else:
pop_new.append(pop_full[id2])
self.pop = self.get_sorted_strim_population(pop_new)
# Get accepted faithful
accepted = self.pop[:self.dyn_accepted_num]
# Update belief_space
self.dyn_belief_space = self.update_belief_space(self.dyn_belief_space, accepted)