#!/usr/bin/env python
# Created by "Thieu" at 04:43, 02/03/2021 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import numpy as np
from mealpy.optimizer import Optimizer
[docs]class OriginalDO(Optimizer):
"""
The original version of: Dragonfly Optimization (DO)
Links:
1. https://link.springer.com/article/10.1007/s00521-015-1920-1
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, DO
>>>
>>> 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 = DO.OriginalDO(epoch=1000, pop_size=50)
>>> 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] Mirjalili, S., 2016. Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective,
discrete, and multi-objective problems. Neural computing and applications, 27(4), pp.1053-1073.
"""
def __init__(self, epoch: int = 10000, pop_size: int = 100, **kwargs: object) -> None:
"""
Args:
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size, default = 100
"""
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.set_parameters(["epoch", "pop_size"])
self.sort_flag = False
[docs] def initialization(self):
if self.pop is None:
self.pop = self.generate_population(self.pop_size)
self.pop_delta = self.generate_population(self.pop_size)
# Initial radius of dragonflies' neighborhoods
self.radius = (self.problem.ub - self.problem.lb) / 10
self.delta_max = (self.problem.ub - self.problem.lb) / 10
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
_, (self.g_best, ), (self.g_worst, ) = self.get_special_agents(self.pop, n_best=1, n_worst=1, minmax=self.problem.minmax)
r = (self.problem.ub - self.problem.lb) / 4 + ((self.problem.ub - self.problem.lb) * (2 * epoch / self.epoch))
w = 0.9 - epoch * ((0.9 - 0.4) / self.epoch)
my_c = 0.1 - epoch * ((0.1 - 0) / (self.epoch / 2))
my_c = 0 if my_c < 0 else my_c
s = 2 * self.generator.random() * my_c # Seperation weight
a = 2 * self.generator.random() * my_c # Alignment weight
c = 2 * self.generator.random() * my_c # Cohesion weight
f = 2 * self.generator.random() # Food attraction weight
e = my_c # Enemy distraction weight
pop_new = []
pop_delta_new = []
for idx in range(0, self.pop_size):
pos_neighbours = []
pos_neighbours_delta = []
neighbours_num = 0
# Find the neighbouring solutions
for j in range(0, self.pop_size):
dist = np.abs(self.pop[idx].solution - self.pop[j].solution)
if np.all(dist <= r) and np.all(dist != 0):
neighbours_num += 1
pos_neighbours.append(self.pop[j].solution)
pos_neighbours_delta.append(self.pop_delta[j].solution)
pos_neighbours = np.array(pos_neighbours)
pos_neighbours_delta = np.array(pos_neighbours_delta)
# Separation: Eq 3.1, Alignment: Eq 3.2, Cohesion: Eq 3.3
if neighbours_num > 1:
S = np.sum(pos_neighbours, axis=0) - neighbours_num * self.pop[idx].solution
A = np.sum(pos_neighbours_delta, axis=0) / neighbours_num
C_temp = np.sum(pos_neighbours, axis=0) / neighbours_num
else:
S = np.zeros(self.problem.n_dims)
A = self.pop_delta[idx].solution.copy()
C_temp = self.pop[idx].solution.copy()
C = C_temp - self.pop[idx].solution
# Attraction to food: Eq 3.4
dist_to_food = np.abs(self.pop[idx].solution - self.g_best.solution)
if np.all(dist_to_food <= r):
F = self.g_best.solution - self.pop[idx].solution
else:
F = np.zeros(self.problem.n_dims)
# Distraction from enemy: Eq 3.5
dist_to_enemy = np.abs(self.pop[idx].solution - self.g_worst.solution)
if np.all(dist_to_enemy <= r):
enemy = self.g_worst.solution + self.pop[idx].solution
else:
enemy = np.zeros(self.problem.n_dims)
pos_new = self.pop[idx].solution.copy().astype(float)
pos_delta_new = self.pop_delta[idx].solution.copy().astype(float)
if np.any(dist_to_food > r):
if neighbours_num > 1:
temp = w * self.pop_delta[idx].solution + self.generator.uniform(0, 1, self.problem.n_dims) * A + \
self.generator.uniform(0, 1, self.problem.n_dims) * C + self.generator.uniform(0, 1, self.problem.n_dims) * S
temp = np.clip(temp, -1 * self.delta_max, self.delta_max)
pos_delta_new = temp.copy()
pos_new += temp
else: # Eq. 3.8
pos_new += self.get_levy_flight_step(beta=1.5, multiplier=0.01, case=-1) * self.pop[idx].solution
pos_delta_new = np.zeros(self.problem.n_dims)
else:
# Eq. 3.6
temp = (a * A + c * C + s * S + f * F + e * enemy) + w * self.pop_delta[idx].solution
temp = np.clip(temp, -1 * self.delta_max, self.delta_max)
pos_delta_new = temp
pos_new += temp
# Amend solution
pos_new = self.correct_solution(pos_new)
pos_delta_new = self.correct_solution(pos_delta_new)
agent = self.generate_empty_agent(pos_new)
agent_delta = self.generate_empty_agent(pos_delta_new)
pop_new.append(agent)
pop_delta_new.append(agent_delta)
if self.mode not in self.AVAILABLE_MODES:
agent.target = self.get_target(pos_new)
agent_delta.target = self.get_target(pos_delta_new)
self.pop[idx] = self.get_better_agent(agent, self.pop[idx], self.problem.minmax)
self.pop_delta[idx] = self.get_better_agent(agent_delta, self.pop_delta[idx], self.problem.minmax)
if self.mode in self.AVAILABLE_MODES:
pop_new = self.update_target_for_population(pop_new)
pop_delta_new = self.update_target_for_population(pop_delta_new)
self.pop = self.greedy_selection_population(self.pop, pop_new, self.problem.minmax)
self.pop_delta = self.greedy_selection_population(self.pop_delta, pop_delta_new, self.problem.minmax)