# !/usr/bin/env python
# Created by "Thieu" at 10:08, 02/03/2021 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import numpy as np
from mealpy.optimizer import Optimizer
[docs]class OriginalHC(Optimizer):
"""
The original version of: Hill Climbing (HC)
Notes
~~~~~
+ The number of neighbour solutions are equal to user defined
+ The step size to calculate neighbour is randomized
Hyper-parameters should fine tuned in approximate range to get faster convergen toward the global optimum:
+ neighbour_size (int): [pop_size/2, pop_size], fixed parameter, sensitive exploitation parameter, Default: 50
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy.math_based.HC import OriginalHC
>>>
>>> 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
>>> neighbour_size = 50
>>> model = OriginalHC(problem_dict1, epoch, pop_size, neighbour_size)
>>> best_position, best_fitness = model.solve()
>>> print(f"Solution: {best_position}, Fitness: {best_fitness}")
References
~~~~~~~~~~
[1] Mitchell, M., Holland, J. and Forrest, S., 1993. When will a genetic algorithm
outperform hill climbing. Advances in neural information processing systems, 6.
"""
def __init__(self, problem, epoch=10000, pop_size=100, neighbour_size=50, **kwargs):
"""
Args:
problem (dict): The problem dictionary
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size, default = 100
neighbour_size (int): fixed parameter, sensitive exploitation parameter, Default: 50
"""
super().__init__(problem, kwargs)
self.nfe_per_epoch = pop_size
self.sort_flag = False
self.epoch = epoch
self.pop_size = pop_size
self.neighbour_size = neighbour_size
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
self.nfe_per_epoch = self.neighbour_size
step_size = np.mean(self.problem.ub - self.problem.lb) * np.exp(-2 * (epoch + 1) / self.epoch)
pop_neighbours = []
for i in range(0, self.neighbour_size):
pos_new = self.g_best[self.ID_POS] + np.random.normal(0, 1, self.problem.n_dims) * step_size
pos_new = self.amend_position(pos_new)
pop_neighbours.append([pos_new, None])
self.pop = self.update_fitness_population(pop_neighbours)
[docs]class BaseHC(OriginalHC):
"""
My changed version of: Swarm-based Hill Climbing (S-HC)
Notes
~~~~~
+ Based on swarm-of people are trying to climb on the mountain idea
+ The number of neighbour solutions are equal to population size
+ The step size to calculate neighbour is randomized and based on rank of solution.
+ The guys near on top of mountain will move slower than the guys on bottom of mountain.
+ Imagination: exploration when far from global best, and exploitation when near global best
+ Who on top of mountain first will be the winner. (global optimal)
Hyper-parameters should fine tuned in approximate range to get faster convergen toward the global optimum:
+ neighbour_size (int): [pop_size/2, pop_size], fixed parameter, sensitive exploitation parameter, Default: 50
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy.math_based.HC import BaseHC
>>>
>>> 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
>>> neighbour_size = 50
>>> model = BaseHC(problem_dict1, epoch, pop_size, neighbour_size)
>>> best_position, best_fitness = model.solve()
>>> print(f"Solution: {best_position}, Fitness: {best_fitness}")
"""
def __init__(self, problem, epoch=10000, pop_size=100, neighbour_size=50, **kwargs):
"""
Args:
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size, default = 100
neighbour_size (int): fixed parameter, sensitive exploitation parameter, Default: 50
"""
super().__init__(problem, epoch, pop_size, neighbour_size, **kwargs)
self.nfe_per_epoch = pop_size
self.sort_flag = True
[docs] def evolve(self, epoch):
"""
Args:
epoch (int): The current iteration
"""
ranks = np.array(list(range(1, self.pop_size + 1)))
ranks = ranks / sum(ranks)
step_size = np.mean(self.problem.ub - self.problem.lb) * np.exp(-2 * (epoch + 1) / self.epoch)
for idx in range(0, self.pop_size):
ss = step_size * ranks[idx]
pop_neighbours = []
for j in range(0, self.neighbour_size):
pos_new = self.pop[idx][self.ID_POS] + np.random.normal(0, 1, self.problem.n_dims) * ss
pos_new = self.amend_position(pos_new)
pop_neighbours.append([pos_new, None])
pop_neighbours = self.update_fitness_population(pop_neighbours)
_, agent = self.get_global_best_solution(pop_neighbours)
self.pop[idx] = agent