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chore(opytimizer): Adds Chernobyl Disaster Optimizer
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‎examples/optimizers/science/create_cdo.py

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from opytimizer.optimizers.science import CDO
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# Creates a CDO optimizer
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o = CDO()

‎opytimizer/optimizers/science/__init__.py

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from opytimizer.optimizers.science.aig import AIG
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from opytimizer.optimizers.science.aso import ASO
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from opytimizer.optimizers.science.bh import BH
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from opytimizer.optimizers.science.cdo import CDO
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from opytimizer.optimizers.science.efo import EFO
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from opytimizer.optimizers.science.eo import EO
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from opytimizer.optimizers.science.esa import ESA

‎opytimizer/optimizers/science/cdo.py

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"""Chernobyl Disaster Optimizer.
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"""
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import copy
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from typing import Any, Dict, Optional, Tuple
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import numpy as np
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import opytimizer.math.random as r
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import opytimizer.utils.constant as c
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from opytimizer.core.function import Function
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from opytimizer.core.space import Space
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from opytimizer.core import Optimizer
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from opytimizer.utils import logging
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logger = logging.get_logger(__name__)
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class CDO(Optimizer):
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"""An CDO class, inherited from Optimizer.
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This is the designed class to define CDO-related
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variables and methods.
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References:
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H. Abedinpourshotorban et al.
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Electromagnetic field optimization: A physics-inspired metaheuristic optimization algorithm.
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Swarm and Evolutionary Computation (2016).
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"""
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def __init__(self, params: Optional[Dict[str, Any]] = None) -> None:
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"""Initialization method.
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Args:
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params: Contains key-value parameters to the meta-heuristics.
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"""
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super(CDO, self).__init__()
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self.build(params)
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logger.info("Class overrided.")
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def compile(self, space: Space) -> None:
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"""Compiles additional information that is used by this optimizer.
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Args:
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space: A Space object containing meta-information.
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"""
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self.gamma_pos = np.zeros((space.n_variables, space.n_dimensions))
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self.gamma_fit = c.FLOAT_MAX
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self.beta_pos = np.zeros((space.n_variables, space.n_dimensions))
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self.beta_fit = c.FLOAT_MAX
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self.alpha_pos = np.zeros((space.n_variables, space.n_dimensions))
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self.alpha_fit = c.FLOAT_MAX
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def update(self, space: Space, function: Function, iteration: int, n_iterations: int) -> None:
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"""Wraps Chernobyl Disaster Optimizer over all agents and variables.
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Args:
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space: Space containing agents and update-related information.
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iteration: Current iteration.
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n_iterations: Maximum number of iterations.
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"""
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for agent in space.agents:
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fit = function(agent.position)
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if fit < self.alpha_fit:
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self.alpha_fit = fit
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self.alpha_pos = copy.deepcopy(agent.position)
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if fit < self.alpha_fit and fit < self.beta_fit:
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self.beta_fit = fit
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self.beta_pos = copy.deepcopy(agent.position)
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if fit < self.alpha_fit and fit < self.beta_fit and fit < self.gamma_fit:
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self.gamma_fit = fit
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self.gamma_pos = copy.deepcopy(agent.position)
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ws = 3 - 3 * iteration/n_iterations
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s_gamma = np.log10(r.generate_uniform_random_number(1, 300000))
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s_beta = np.log10(r.generate_uniform_random_number(1, 270000))
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s_alpha = np.log10(r.generate_uniform_random_number(1, 16000))
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for agent in space.agents:
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r1 = r.generate_uniform_random_number(
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size=(space.n_variables, space.n_dimensions)
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)
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r2 = r.generate_uniform_random_number(
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size=(space.n_variables, space.n_dimensions)
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)
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r3 = r.generate_uniform_random_number(
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size=(space.n_variables, space.n_dimensions)
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)
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rho_gamma = np.pi * r1 * r1 / s_gamma - ws * r2
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a_gamma = r3 * r3 * np.pi
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grad_gamma = np.abs(a_gamma * self.gamma_pos - agent.position)
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v_gamma = agent.position - rho_gamma * grad_gamma
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r1 = r.generate_uniform_random_number(
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size=(space.n_variables, space.n_dimensions)
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)
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r2 = r.generate_uniform_random_number(
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size=(space.n_variables, space.n_dimensions)
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)
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r3 = r.generate_uniform_random_number(
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size=(space.n_variables, space.n_dimensions)
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)
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rho_beta = np.pi * r1 * r1 / (0.5 * s_beta) - ws * r2
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a_beta = r3 * r3 * np.pi
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grad_beta = np.abs(a_beta * self.beta_pos - agent.position)
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v_beta = 0.5 * (agent.position - rho_beta * grad_beta)
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r1 = r.generate_uniform_random_number(
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size=(space.n_variables, space.n_dimensions)
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)
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r2 = r.generate_uniform_random_number(
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size=(space.n_variables, space.n_dimensions)
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)
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r3 = r.generate_uniform_random_number(
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size=(space.n_variables, space.n_dimensions)
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)
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rho_alpha = np.pi * r1 * r1 / (0.25 * s_alpha) - ws * r2
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a_alpha = r3 * r3 * np.pi
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grad_alpha = np.abs(a_alpha * self.alpha_pos - agent.position)
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v_alpha = 0.25 * (agent.position - rho_alpha * grad_alpha)
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agent.position = (v_alpha + v_beta + v_gamma) / 3

‎tests/opytimizer/optimizers/science/test_cdo.py

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import numpy as np
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from opytimizer.optimizers.science import cdo
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from opytimizer.spaces import search
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def test_cdo_update():
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def square(x):
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return np.sum(x**2)
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new_cdo = cdo.CDO()
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search_space = search.SearchSpace(
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n_agents=20, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]
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)
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new_cdo.update(search_space, square)

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