#!/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 mealpy.optimizer import Optimizer
[docs]class OriginalPFA(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 import FloatVar, PFA
>>>
>>> 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 = PFA.OriginalPFA(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] Yapici, H. and Cetinkaya, N., 2019. A new meta-heuristic optimizer: Pathfinder algorithm.
Applied soft computing, 78, pp.545-568.
"""
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 = True
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
alpha, beta = self.generator.uniform(1, 2, 2)
A = self.generator.uniform(self.problem.lb, self.problem.ub) * np.exp(-2 * epoch / self.epoch)
t = 1. - epoch * 1.0 / self.epoch
space = self.problem.ub - self.problem.lb
## Update the position of pathfinder and check the bound
pos_new = self.pop[0].solution + 2 * self.generator.uniform() * (self.g_best.solution - self.pop[0].solution) + A
pos_new = self.correct_solution(pos_new)
agent = self.generate_agent(pos_new)
pop_new = [agent, ]
## Update positions of members, check the bound and calculate new fitness
for idx in range(1, self.pop_size):
pos_new = self.pop[idx].solution.copy().astype(float)
for k in range(1, self.pop_size):
dist = np.sqrt(np.sum((self.pop[k].solution - self.pop[idx].solution) ** 2)) / self.problem.n_dims
t2 = alpha * self.generator.uniform() * (self.pop[k].solution - self.pop[idx].solution)
## First stabilize the distance
t3 = self.generator.uniform() * t * (dist / space)
pos_new += t2 + t3
## Second stabilize the population size
t1 = beta * self.generator.uniform() * (self.g_best.solution - self.pop[idx].solution)
pos_new = (pos_new + t1) / self.pop_size
pos_new = self.correct_solution(pos_new)
agent = self.generate_empty_agent(pos_new)
pop_new.append(agent)
if self.mode not in self.AVAILABLE_MODES:
pop_new[-1].target = self.get_target(pos_new)
if self.mode in self.AVAILABLE_MODES:
pop_new = self.update_target_for_population(pop_new)
self.pop = self.greedy_selection_population(self.pop, pop_new, self.problem.minmax)