#!/usr/bin/env python
# Created by "Thieu" at 22:07, 11/04/2020 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import numpy as np
from mealpy.optimizer import Optimizer
[docs]class DevVCS(Optimizer):
"""
The developed version: Virus Colony Search (VCS)
Links:
1. https://doi.org/10.1016/j.advengsoft.2015.11.004
Notes:
+ In Immune response process, updates the whole position instead of updating each variable in position
Hyper-parameters should fine-tune in approximate range to get faster convergence toward the global optimum:
+ lamda (float): (0, 1.0) -> better [0.2, 0.5], Percentage of the number of the best will keep, default = 0.5
+ sigma (float): (0, 5.0) -> better [0.1, 2.0], Weight factor
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, VCS
>>>
>>> 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 = VCS.DevVCS(epoch=1000, pop_size=50, lamda = 0.5, sigma = 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}")
"""
def __init__(self, epoch: int = 10000, pop_size: int = 100, lamda: float = 0.5, sigma: float = 1.5, **kwargs: object) -> None:
"""
Args:
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size, default = 100
lamda (float): Percentage of the number of the best will keep, default = 0.5
sigma (float): Weight factor, default = 1.5
"""
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.lamda = self.validator.check_float("lamda", lamda, (0, 1.0))
self.sigma = self.validator.check_float("sigma", sigma, (0, 5.0))
self.n_best = int(self.lamda * self.pop_size)
self.set_parameters(["epoch", "pop_size", "lamda", "sigma"])
self.sort_flag = True
[docs] def calculate_xmean__(self, pop):
"""
Calculate the mean position of list of solutions (population)
Args:
pop (list): List of solutions (population)
Returns:
list: Mean position
"""
## Calculate the weighted mean of the λ best individuals by
pop = self.get_sorted_population(pop, self.problem.minmax)
pos_list = [agent.solution for agent in pop[:self.n_best]]
factor_down = self.n_best * np.log1p(self.n_best + 1) - np.log1p(np.prod(range(1, self.n_best + 1)))
weight = np.log1p(self.n_best + 1) / factor_down
weight = weight / self.n_best
x_mean = weight * np.sum(pos_list, axis=0)
return x_mean
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
## Viruses diffusion
pop = []
for idx in range(0, self.pop_size):
sigma = (np.log1p(epoch + 1) / self.epoch) * (self.pop[idx].solution - self.g_best.solution)
gauss = self.generator.normal(self.generator.normal(self.g_best.solution, np.abs(sigma)))
pos_new = gauss + self.generator.uniform() * self.g_best.solution - self.generator.uniform() * self.pop[idx].solution
pos_new = self.correct_solution(pos_new)
agent = self.generate_empty_agent(pos_new)
pop.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 = self.update_target_for_population(pop)
self.pop = self.greedy_selection_population(self.pop, pop, self.problem.minmax)
## Host cells infection
x_mean = self.calculate_xmean__(self.pop)
sigma = self.sigma * (1 - epoch / self.epoch)
pop = []
for idx in range(0, self.pop_size):
## Basic / simple version, not the original version in the paper
pos_new = x_mean + sigma * self.generator.normal(0, 1, self.problem.n_dims)
pos_new = self.correct_solution(pos_new)
agent = self.generate_empty_agent(pos_new)
pop.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 = self.update_target_for_population(pop)
self.pop = self.greedy_selection_population(self.pop, pop, self.problem.minmax)
## Calculate the weighted mean of the λ best individuals by
self.pop = self.get_sorted_population(self.pop, self.problem.minmax)
## Immune response
pop = []
for idx in range(0, self.pop_size):
pr = (self.problem.n_dims - idx + 1) / self.problem.n_dims
id1, id2 = self.generator.choice(list(set(range(0, self.pop_size)) - {idx}), 2, replace=False)
temp = self.pop[id1].solution - (self.pop[id2].solution - self.pop[idx].solution) * self.generator.uniform()
condition = self.generator.random(self.problem.n_dims) < pr
pos_new = np.where(condition, self.pop[idx].solution, temp)
pos_new = self.correct_solution(pos_new)
agent = self.generate_empty_agent(pos_new)
pop.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 = self.update_target_for_population(pop)
self.pop = self.greedy_selection_population(self.pop, pop, self.problem.minmax)
[docs]class OriginalVCS(DevVCS):
"""
The original version of: Virus Colony Search (VCS)
Links:
1. https://doi.org/10.1016/j.advengsoft.2015.11.004
Hyper-parameters should fine-tune in approximate range to get faster convergence toward the global optimum:
+ lamda (float): (0, 1.0) -> better [0.2, 0.5], Percentage of the number of the best will keep, default = 0.5
+ sigma (float): (0, 5.0) -> better [0.1, 2.0], Weight factor
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, VCS
>>>
>>> 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 = VCS.OriginalVCS(epoch=1000, pop_size=50, lamda = 0.5, sigma = 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] Li, M.D., Zhao, H., Weng, X.W. and Han, T., 2016. A novel nature-inspired algorithm
for optimization: Virus colony search. Advances in Engineering Software, 92, pp.65-88.
"""
def __init__(self, epoch: int = 10000, pop_size: int = 100, lamda: float = 0.5, sigma: float = 1.5, **kwargs: object) -> None:
"""
Args:
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size, default = 100
lamda (float): Number of the best will keep, default = 0.5
sigma (float): Weight factor, default = 1.5
"""
super().__init__(epoch, pop_size, lamda, sigma, **kwargs)
[docs] def amend_solution(self, solution: np.ndarray) -> np.ndarray:
condition = np.clip(solution, self.problem.lb, self.problem.ub)
rand_pos = self.generator.uniform(self.problem.lb, self.problem.ub)
return np.where(condition, solution, rand_pos)
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
## Viruses diffusion
pop = []
for idx in range(0, self.pop_size):
sigma = (np.log1p(epoch) / self.epoch) * (self.pop[idx].solution - self.g_best.solution)
gauss = np.array([self.generator.normal(self.g_best.solution[j], np.abs(sigma[j])) for j in range(0, self.problem.n_dims)])
pos_new = gauss + self.generator.uniform() * self.g_best.solution - self.generator.uniform() * self.pop[idx].solution
pos_new = self.correct_solution(pos_new)
agent = self.generate_empty_agent(pos_new)
pop.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 = self.update_target_for_population(pop)
self.pop = self.greedy_selection_population(self.pop, pop, self.problem.minmax)
## Host cells infection
x_mean = self.calculate_xmean__(self.pop)
sigma = self.sigma * (1 - epoch / self.epoch)
pop = []
for idx in range(0, self.pop_size):
## Basic / simple version, not the original version in the paper
pos_new = x_mean + sigma * self.generator.normal(0, 1, self.problem.n_dims)
pos_new = self.correct_solution(pos_new)
agent = self.generate_empty_agent(pos_new)
pop.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 = self.update_target_for_population(pop)
self.pop = self.greedy_selection_population(self.pop, pop, self.problem.minmax)
## Immune response
pop = []
for idx in range(0, self.pop_size):
pr = (self.problem.n_dims - idx + 1) / self.problem.n_dims
pos_new = self.pop[idx].solution.copy()
for j in range(0, self.problem.n_dims):
if self.generator.uniform() > pr:
id1, id2 = self.generator.choice(list(set(range(0, self.pop_size)) - {idx}), 2, replace=False)
pos_new[j] = self.pop[id1].solution[j] - (self.pop[id2].solution[j] - self.pop[idx].solution[j]) * self.generator.uniform()
pos_new = self.correct_solution(pos_new)
agent = self.generate_empty_agent(pos_new)
pop.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 = self.update_target_for_population(pop)
self.pop = self.greedy_selection_population(self.pop, pop, self.problem.minmax)