#!/usr/bin/env python
# Created by "Thieu" at 12:17, 18/03/2020 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import numpy as np
from mealpy.optimizer import Optimizer
from mealpy.utils.opt_info import OptInfo
[docs]class OriginalIWO(Optimizer):
"""
The original version of: Invasive Weed Optimization (IWO)
Parameters
----------
epoch : int
Maximum number of iterations, in range [1, 100000]. Default is 10000.
pop_size : int
Number of population size, in range [5, 10000]. Default is 100.
seed_min : int
Number of Seeds (min), in range [1, 3]. Default is 2.
seed_max : int
Number of seeds (max), in range [4, int(pop_size/2)]. Default is 10.
exponent : int
Variance Reduction Exponent, in range [2, 4]. Default is 2.
sigma_start : float
The initial value of standard deviation, in range [0.5, 5.0]. Default is 1.0.
sigma_end : float
The final value of standard deviation, in range (0.0, 0.5). Default is 0.01.
Note
----
Better to use normal distribution instead of uniform distribution, updating population by sorting
both parent population and child population
References
~~~~~~~~~~
1. Mehrabian, A.R. and Lucas, C., 2006.
A novel numerical optimization algorithm inspired from weed colonization.
Ecological informatics, 1(4), pp.355-366. https://doi.org/10.1016/j.ecoinf.2006.07.003
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, IWO
>>>
>>> def objective_function(solution):
>>> return np.sum(solution**2)
>>>
>>> problem_dict = {
>>> "bounds": FloatVar(lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
>>> "minmax": "min",
>>> "obj_func": objective_function
>>> }
>>>
>>> model = IWO.OriginalIWO(epoch=1000, pop_size=50, seed_min = 3, seed_max = 9, exponent = 3, sigma_start = 0.6, sigma_end = 0.01)
>>> 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}")
"""
OPT_INFO = OptInfo(name="Invasive Weed Optimization", year=2006, difficulty="medium", kind="original")
def __init__(self, epoch: int = 10000, pop_size: int = 100, seed_min: int = 2, seed_max: int = 10,
exponent: int = 2, sigma_start: float = 1.0, sigma_end: float = 0.01, **kwargs: object) -> None:
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.seed_min = self.validator.check_int("seed_min", seed_min, [1, 3])
self.seed_max = self.validator.check_int("seed_max", seed_max, [4, int(self.pop_size/2)])
self.exponent = self.validator.check_int("exponent", exponent, [2, 4])
self.sigma_start = self.validator.check_float("sigma_start", sigma_start, [0.5, 5.0])
self.sigma_end = self.validator.check_float("sigma_end", sigma_end, (0, 0.5))
self.set_parameters(["epoch", "pop_size", "seed_min", "seed_max", "exponent", "sigma_start", "sigma_end"])
self.sort_flag = True
[docs] def evolve(self, epoch=None):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
# Update Standard Deviation
sigma = (1. - epoch/self.epoch) ** self.exponent * (self.sigma_start - self.sigma_end) + self.sigma_end
pop, list_best, list_worst = self.get_special_agents(self.pop, n_best=1, n_worst=1, minmax=self.problem.minmax)
best, worst = list_best[0], list_worst[0]
pop_new = []
for idx in range(0, self.pop_size):
temp = best.target.fitness - worst.target.fitness
if temp == 0:
ratio = self.generator.random()
else:
ratio = (pop[idx].target.fitness - worst.target.fitness) / temp
s = int(np.ceil(self.seed_min + (self.seed_max - self.seed_min) * ratio))
if s > int(np.sqrt(self.pop_size)):
s = int(np.sqrt(self.pop_size))
pop_local = []
for jdx in range(s):
# Initialize Offspring and Generate Random Location
pos_new = pop[idx].solution + 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_local.append(agent)
if self.mode not in self.AVAILABLE_MODES:
pop_local[-1].target = self.get_target(pos_new)
if self.mode in self.AVAILABLE_MODES:
pop_local = self.update_target_for_population(pop_local)
pop_new += pop_local
self.pop = self.get_sorted_and_trimmed_population(pop_new, self.pop_size, self.problem.minmax)