#!/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-tune in approximate range to get faster convergence 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 import FloatVar, CA
>>>
>>> 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 = CA.OriginalCA(epoch=1000, pop_size=50, accepted_rate = 0.15)
>>> 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] 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, epoch: int = 10000, pop_size: int = 100, accepted_rate: float = 0.15, **kwargs: object) -> None:
"""
Args:
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__(**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.accepted_rate = self.validator.check_float("accepted_rate", accepted_rate, (0, 1.0))
self.set_parameters(["epoch", "pop_size", "accepted_rate"])
self.is_parallelizable = False
self.sort_flag = True
[docs] def initialize_variables(self):
## 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 an element inside belief space)
[docs] def create_faithful__(self, lb, ub):
pos = self.generator.uniform(lb, ub)
return self.generate_agent(pos)
[docs] def update_belief_space__(self, belief_space, pop_accepted):
pos_list = np.array([agent.solution for agent 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 = self.generator.choice(list(range(0, size_new)), 2, replace=False)
agent = self.get_better_agent(pop_full[id1], pop_full[id2], self.problem.minmax)
pop_new.append(agent)
self.pop = self.get_sorted_population(pop_new, self.problem.minmax)
# 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)