# !/usr/bin/env python
# Created by "Thieu" at 14:51, 17/03/2020 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import numpy as np
from copy import deepcopy
from mealpy.optimizer import Optimizer
[docs]class BasePFA(Optimizer):
"""
The original version of: Pathfinder Algorithm (PFA)
Links:
1. https://doi.org/10.1016/j.asoc.2019.03.012
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy.swarm_based.PFA import BasePFA
>>>
>>> def fitness_function(solution):
>>> return np.sum(solution**2)
>>>
>>> problem_dict1 = {
>>> "fit_func": fitness_function,
>>> "lb": [-10, -15, -4, -2, -8],
>>> "ub": [10, 15, 12, 8, 20],
>>> "minmax": "min",
>>> "verbose": True,
>>> }
>>>
>>> epoch = 1000
>>> pop_size = 50
>>> model = BasePFA(problem_dict1, epoch, pop_size)
>>> best_position, best_fitness = model.solve()
>>> print(f"Solution: {best_position}, Fitness: {best_fitness}")
References
~~~~~~~~~~
[1] Yapici, H. and Cetinkaya, N., 2019. A new meta-heuristic optimizer: Pathfinder algorithm.
Applied soft computing, 78, pp.545-568.
"""
def __init__(self, problem, epoch=10000, pop_size=100, **kwargs):
"""
Args:
problem (dict): The problem dictionary
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size, default = 100
"""
super().__init__(problem, kwargs)
self.nfe_per_epoch = pop_size
self.sort_flag = True
self.epoch = epoch
self.pop_size = pop_size
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
alpha, beta = np.random.uniform(1, 2, 2)
A = np.random.uniform(self.problem.lb, self.problem.ub) * np.exp(-2 * (epoch + 1) / self.epoch)
## Update the position of pathfinder and check the bound
pos_new = self.pop[0][self.ID_POS] + 2 * np.random.uniform() * (self.g_best[self.ID_POS] - self.pop[0][self.ID_POS]) + A
pos_new = self.amend_position(pos_new)
fit = self.get_fitness_position(pos_new)
pop_new = [[pos_new, fit], ]
## Update positions of members, check the bound and calculate new fitness
for idx in range(1, self.pop_size):
pos_new = deepcopy(self.pop[idx][self.ID_POS]).astype(float)
for k in range(1, self.pop_size):
dist = np.sqrt(np.sum((self.pop[k][self.ID_POS] - self.pop[idx][self.ID_POS]) ** 2)) / self.problem.n_dims
t2 = alpha * np.random.uniform() * (self.pop[k][self.ID_POS] - self.pop[idx][self.ID_POS])
## First stabilize the distance
t3 = np.random.uniform() * (1 - (epoch + 1) * 1.0 / self.epoch) * (dist / (self.problem.ub - self.problem.lb))
pos_new += t2 + t3
## Second stabilize the population size
t1 = beta * np.random.uniform() * (self.g_best[self.ID_POS] - self.pop[idx][self.ID_POS])
pos_new = (pos_new + t1) / self.pop_size
pos_new = self.amend_position(pos_new)
pop_new.append([pos_new, None])
pop_new = self.update_fitness_population(pop_new)
self.pop = self.greedy_selection_population(self.pop, pop_new)