#!/usr/bin/env python
# Created by "Thieu" at 14:07, 02/03/2021 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import numpy as np
from mealpy.optimizer import Optimizer
[docs]class OriginalICA(Optimizer):
"""
The original version of: Imperialist Competitive Algorithm (ICA)
Links:
1. https://ieeexplore.ieee.org/document/4425083
Hyper-parameters should fine-tune in approximate range to get faster convergence toward the global optimum:
+ empire_count (int): [3, 10], Number of Empires (also Imperialists)
+ assimilation_coeff (float): [1.0, 3.0], Assimilation Coefficient (beta in the paper)
+ revolution_prob (float): [0.01, 0.1], Revolution Probability
+ revolution_rate (float): [0.05, 0.2], Revolution Rate (mu)
+ revolution_step_size (float): [0.05, 0.2], Revolution Step Size (sigma)
+ zeta (float): [0.05, 0.2], Colonies Coefficient in Total Objective Value of Empires
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, ICA
>>>
>>> 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 = ICA.OriginalICA(epoch=1000, pop_size=50, empire_count = 5, assimilation_coeff = 1.5,
>>> revolution_prob = 0.05, revolution_rate = 0.1, revolution_step_size = 0.1, zeta = 0.1)
>>> 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] Atashpaz-Gargari, E. and Lucas, C., 2007, September. Imperialist competitive algorithm: an algorithm for
optimization inspired by imperialistic competition. In 2007 IEEE congress on evolutionary computation (pp. 4661-4667). Ieee.
"""
def __init__(self, epoch: int = 10000, pop_size: int = 100, empire_count: int = 5, assimilation_coeff: float = 1.5, revolution_prob: float = 0.05,
revolution_rate: float = 0.1, revolution_step_size: float = 0.1, zeta: float = 0.1, **kwargs: object) -> None:
"""
Args:
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size (n: pop_size, m: clusters), default = 100
empire_count (int): Number of Empires (also Imperialists)
assimilation_coeff (float): Assimilation Coefficient (beta in the paper)
revolution_prob (float): Revolution Probability
revolution_rate (float): Revolution Rate (mu)
revolution_step_size (float): Revolution Step Size (sigma)
zeta (float): Colonies Coefficient in Total Objective Value of Empires
"""
super().__init__(**kwargs)
self.epoch = self.validator.check_int("epoch", epoch, [1, 100000])
self.pop_size = self.validator.check_int("pop_size", pop_size, [10, 10000])
self.empire_count = self.validator.check_int("empire_count", empire_count, [2, 2 + int(self.pop_size / 5)])
self.assimilation_coeff = self.validator.check_float("assimilation_coeff", assimilation_coeff, [1.0, 3.0])
self.revolution_prob = self.validator.check_float("revolution_prob", revolution_prob, (0, 1.0))
self.revolution_rate = self.validator.check_float("revolution_rate", revolution_rate, (0, 1.0))
self.revolution_step_size = self.validator.check_float("revolution_step_size", revolution_step_size, (0, 1.0))
self.zeta = self.validator.check_float("zeta", zeta, (0, 1.0))
self.set_parameters(["epoch", "pop_size", "empire_count", "assimilation_coeff", "revolution_prob",
"revolution_rate", "revolution_step_size", "zeta"])
self.sort_flag = True
[docs] def revolution_country__(self, solution: np.ndarray, n_revoluted: int) -> np.ndarray:
pos_new = solution + self.revolution_step_size * self.generator.normal(0, 1, self.problem.n_dims)
idx_list = self.generator.choice(range(0, self.problem.n_dims), n_revoluted, replace=False)
if len(idx_list) == 0:
idx_list = np.append(idx_list, self.generator.integers(0, self.problem.n_dims))
solution[idx_list] = pos_new[idx_list] # Change only those selected index
return solution
[docs] def initialization(self):
if self.pop is None:
self.pop = self.generate_population(self.pop_size)
self.pop = self.get_sorted_population(self.pop, self.problem.minmax)
self.g_best = self.pop[0].copy()
# Initialization
self.n_revoluted_variables = int(round(self.revolution_rate * self.problem.n_dims))
# pop = Empires
colony_count = self.pop_size - self.empire_count
self.pop_empires = [agent.copy() for agent in self.pop[:self.empire_count]]
self.pop_colonies = [agent.copy() for agent in self.pop[self.empire_count:]]
cost_empires_list = np.array([agent.target.fitness for agent in self.pop_empires])
cost_empires_list_normalized = cost_empires_list - (np.max(cost_empires_list) + np.min(cost_empires_list))
prob_empires_list = np.abs(cost_empires_list_normalized / np.sum(cost_empires_list_normalized))
# Randomly choose colonies to empires
self.empires = {}
idx_already_selected = []
for i in range(0, self.empire_count - 1):
self.empires[i] = []
n_colonies = int(round(prob_empires_list[i] * colony_count))
idx_list = self.generator.choice(list(set(range(0, colony_count)) - set(idx_already_selected)), n_colonies, replace=False).tolist()
idx_already_selected += idx_list
for idx in idx_list:
self.empires[i].append(self.pop_colonies[idx])
idx_last = list(set(range(0, colony_count)) - set(idx_already_selected))
self.empires[self.empire_count - 1] = []
for idx in idx_last:
self.empires[self.empire_count - 1].append(self.pop_colonies[idx].copy())
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
# Assimilation
for idx, colonies in self.empires.items():
for idx_colony, colony in enumerate(colonies):
pos_new = colony.solution + self.assimilation_coeff * \
self.generator.uniform(0, 1, self.problem.n_dims) * (self.pop_empires[idx].solution - colony.solution)
pos_new = self.correct_solution(pos_new)
self.empires[idx][idx_colony].solution = pos_new
if self.mode not in self.AVAILABLE_MODES:
self.empires[idx][idx_colony].target = self.get_target(pos_new)
self.empires[idx] = self.update_target_for_population(self.empires[idx])
# Revolution
for idx, colonies in self.empires.items():
# Apply revolution to Imperialist
pos_new_em = self.revolution_country__(self.pop_empires[idx].solution, self.n_revoluted_variables)
pos_new_em = self.correct_solution(pos_new_em)
self.pop_empires[idx].solution = pos_new_em
if self.mode not in self.AVAILABLE_MODES:
self.pop_empires[idx].target = self.get_target(pos_new_em)
# Apply revolution to Colonies
for idx_colony, colony in enumerate(colonies):
if self.generator.random() < self.revolution_prob:
pos_new = self.revolution_country__(colony.solution, self.n_revoluted_variables)
pos_new = self.correct_solution(pos_new)
self.empires[idx][idx_colony].solution = pos_new
if self.mode not in self.AVAILABLE_MODES:
self.empires[idx][idx_colony].target = self.get_target(pos_new)
self.empires[idx] = self.update_target_for_population(self.empires[idx])
self.pop_empires = self.update_target_for_population(self.pop_empires)
self.update_global_best_agent(self.pop_empires, save=False)
# Intra-Empire Competition
for idx, colonies in self.empires.items():
for idx_colony, colony in enumerate(colonies):
if self.compare_target(colony.target, self.pop_empires[idx].target, self.problem.minmax):
self.empires[idx][idx_colony], self.pop_empires[idx] = self.pop_empires[idx].copy(), colony.copy()
# Update Total Objective Values of Empires
cost_empires_list = []
for idx, colonies in self.empires.items():
fit_list = np.array([agent.target.fitness for agent in colonies])
fit_empire = self.pop_empires[idx].target.fitness + self.zeta * np.mean(fit_list)
cost_empires_list.append(fit_empire)
cost_empires_list = np.array(cost_empires_list)
# Find possession probability of each empire based on its total power
cost_empires_list_normalized = cost_empires_list - (np.max(cost_empires_list) + np.min(cost_empires_list))
prob_empires_list = np.abs(cost_empires_list_normalized / np.sum(cost_empires_list_normalized)) # Vector P
uniform_list = self.generator.uniform(0, 1, len(prob_empires_list)) # Vector R
vector_D = prob_empires_list - uniform_list
idx_empire = np.argmax(vector_D)
# Find the weakest empire and weakest colony inside it
idx_weakest_empire = np.argmax(cost_empires_list)
if len(self.empires[idx_weakest_empire]) > 0:
colonies_sorted = self.get_sorted_population(self.empires[idx_weakest_empire], self.problem.minmax)
self.empires[idx_empire].append(colonies_sorted.pop(-1))
else:
self.empires[idx_empire].append(self.pop_empires.pop(idx_weakest_empire))
self.pop = self.pop_empires + self.pop_colonies