#!/usr/bin/env python
# Created by "Thieu" at 17:13, 01/03/2021 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import numpy as np
from mealpy.optimizer import Optimizer
[docs]class OriginalFFA(Optimizer):
"""
The original version of: Firefly Algorithm (FFA)
Hyper-parameters should fine-tune in approximate range to get faster convergence toward the global optimum:
+ gamma (float): Light Absorption Coefficient, default = 0.001
+ beta_base (float): Attraction Coefficient Base Value, default = 2
+ alpha (float): Mutation Coefficient, default = 0.2
+ alpha_damp (float): Mutation Coefficient Damp Rate, default = 0.99
+ delta (float): Mutation Step Size, default = 0.05
+ exponent (int): Exponent (m in the paper), default = 2
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, FFA
>>>
>>> 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 = FFA.OriginalFFA(epoch=1000, pop_size=50, gamma = 0.001, beta_base = 2, alpha = 0.2, alpha_damp = 0.99, delta = 0.05, exponent = 2)
>>> 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] Gandomi, A.H., Yang, X.S. and Alavi, A.H., 2011. Mixed variable structural optimization
using firefly algorithm. Computers & Structures, 89(23-24), pp.2325-2336.
[2] Arora, S. and Singh, S., 2013. The firefly optimization algorithm: convergence analysis and
parameter selection. International Journal of Computer Applications, 69(3).
"""
def __init__(self, epoch: int = 10000, pop_size: int = 100, gamma: float = 0.001, beta_base: float = 2,
alpha: float = 0.2, alpha_damp: float = 0.99, delta: float = 0.05, exponent: int = 2, **kwargs: object) -> None:
"""
Args:
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size, default = 100
gamma (float): Light Absorption Coefficient, default = 0.001
beta_base (float): Attraction Coefficient Base Value, default = 2
alpha (float): Mutation Coefficient, default = 0.2
alpha_damp (float): Mutation Coefficient Damp Rate, default = 0.99
delta (float): Mutation Step Size, default = 0.05
exponent (int): Exponent (m in the paper), default = 2
"""
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.gamma = self.validator.check_float("gamma", gamma, (0, 1.0))
self.beta_base = self.validator.check_float("beta_base", beta_base, (0, 3.0))
self.alpha = self.validator.check_float("alpha", alpha, (0, 1.0))
self.alpha_damp = self.validator.check_float("alpha_damp", alpha_damp, (0, 1.0))
self.delta = self.validator.check_float("delta", delta, (0, 1.0))
self.exponent = self.validator.check_int("exponent", exponent, [2, 4])
self.set_parameters(["epoch", "pop_size", "gamma", "beta_base", "alpha", "alpha_damp", "delta", "exponent"])
self.is_parallelizable = False
self.sort_flag = False
[docs] def initialize_variables(self):
self.dyn_alpha = self.alpha # Initial Value of Mutation Coefficient
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
# Maximum Distance
dmax = np.sqrt(self.problem.n_dims)
for idx in range(0, self.pop_size):
agent = self.pop[idx].copy()
pop_child = []
for j in range(idx + 1, self.pop_size):
# Move Towards Better Solutions
if self.compare_target(self.pop[j].target, agent.target, self.problem.minmax):
# Calculate Radius and Attraction Level
rij = np.linalg.norm(agent.solution - self.pop[j].solution) / dmax
beta = self.beta_base * np.exp(-self.gamma * rij ** self.exponent)
# Mutation Vector
mutation_vector = self.delta * self.generator.uniform(0, 1, self.problem.n_dims)
temp = np.matmul((self.pop[j].solution - agent.solution), self.generator.uniform(0, 1, (self.problem.n_dims, self.problem.n_dims)))
pos_new = agent.solution + self.dyn_alpha * mutation_vector + beta * temp
pos_new = self.correct_solution(pos_new)
agent = self.generate_agent(pos_new)
pop_child.append(agent)
if len(pop_child) < self.pop_size:
pop_child += self.generate_population(self.pop_size - len(pop_child))
local_best = self.get_best_agent(pop_child, self.problem.minmax)
# Compare to Previous Solution
if self.compare_target(local_best.target, agent.target, self.problem.minmax):
self.pop[idx] = local_best
self.pop.append(self.g_best)
self.dyn_alpha = self.alpha_damp * self.alpha