#!/usr/bin/env python
# Created by "Thieu" at 18:37, 28/05/2021 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import numpy as np
from mealpy.optimizer import Optimizer
[docs]class OriginalCSA(Optimizer):
"""
The original version of: Cuckoo Search Algorithm (CSA)
Links:
1. https://doi.org/10.1109/NABIC.2009.5393690
Hyper-parameters should fine-tune in approximate range to get faster convergence toward the global optimum:
+ p_a (float): [0.1, 0.7], probability a, default=0.3
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, CSA
>>>
>>> def objective_function(solution):
>>> return np.sum(solution**2)
>>>
>>> problem_dict = {
>>> "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
>>> "obj_func": objective_function,
>>> "minmax": "min",
>>> }
>>>
>>> model = CSA.OriginalCSA(epoch=1000, pop_size=50, p_a = 0.3)
>>> 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] Yang, X.S. and Deb, S., 2009, December. Cuckoo search via Lévy flights. In 2009 World
congress on nature & biologically inspired computing (NaBIC) (pp. 210-214). Ieee.
"""
def __init__(self, epoch: int = 10000, pop_size: int = 100, p_a: float = 0.3, **kwargs: object) -> None:
"""
Args:
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size, default = 100
p_a (float): probability a, default=0.3
"""
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.p_a = self.validator.check_float("p_a", p_a, (0, 1.0))
self.set_parameters(["epoch", "pop_size", "p_a"])
self.n_cut = int(self.p_a * self.pop_size)
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):
## Generate levy-flight solution
levy_step = self.get_levy_flight_step(multiplier=0.001, case=-1)
pos_new = self.pop[idx].solution + 1.0 / np.sqrt(epoch) * np.sign(self.generator.random() - 0.5) * \
levy_step * (self.pop[idx].solution - self.g_best.solution)
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:
agent.target = self.get_target(pos_new)
self.pop[idx] = self.get_better_agent(agent, self.pop[idx], self.problem.minmax)
if self.mode in self.AVAILABLE_MODES:
pop_new = self.update_target_for_population(pop_new)
self.pop = self.greedy_selection_population(self.pop, pop_new, self.problem.minmax)
## Abandoned some worst nests
pop = self.get_sorted_and_trimmed_population(self.pop, self.pop_size, self.problem.minmax)
pop_new = []
for idx in range(0, self.n_cut):
pos_new = self.generator.uniform(self.problem.lb, self.problem.ub)
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)
pop_new = self.update_target_for_population(pop_new)
self.pop = pop[:(self.pop_size - self.n_cut)] + pop_new