#!/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 group is randomized
+ HC is single-based solution, so the pop_size parameter is not matter in this algorithm
Hyper-parameters should fine-tune in approximate range to get faster convergence toward the global optimum:
+ neighbour_size (int): [2, 1000], fixed parameter, sensitive exploitation parameter, Default: 50
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, HC
>>>
>>> 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 = HC.OriginalHC(epoch=1000, pop_size=50, neighbour_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] 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, epoch: int = 10000, pop_size: int = 2, neighbour_size: int = 50, **kwargs: object) -> None:
"""
Args:
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size, default = 2
neighbour_size (int): fixed parameter, sensitive exploitation parameter, Default: 50
"""
super().__init__(**kwargs)
self.epoch = self.validator.check_int("epoch", epoch, [1, 100000])
self.pop_size = self.validator.check_int("pop_size", pop_size, [2, 10000])
self.neighbour_size = self.validator.check_int("neighbour_size", neighbour_size, [2, 1000])
self.set_parameters(["epoch", "pop_size", "neighbour_size"])
self.sort_flag = False
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
step_size = np.exp(-2 * epoch / self.epoch)
pop_neighbours = []
for idx in range(0, self.neighbour_size):
pos_new = self.g_best.solution + self.generator.uniform(self.problem.lb, self.problem.ub) * step_size
pos_new = self.correct_solution(pos_new)
agent = self.generate_empty_agent(pos_new)
pop_neighbours.append(agent)
if self.mode not in self.AVAILABLE_MODES:
pop_neighbours[-1].target = self.get_target(pos_new)
self.pop = self.update_target_for_population(pop_neighbours)
[docs]class SwarmHC(Optimizer):
"""
The developed version: 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-tune in approximate range to get faster convergence toward the global optimum:
+ neighbour_size (int): [2, pop_size/2], fixed parameter, sensitive exploitation parameter, Default: 10
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, HC
>>>
>>> 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 = HC.SwarmHC(epoch=1000, pop_size=50, neighbour_size = 10)
>>> 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}")
"""
def __init__(self, epoch=10000, pop_size=100, neighbour_size=10, **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: 10
"""
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.neighbour_size = self.validator.check_int("neighbour_size", neighbour_size, [2, int(self.pop_size/2)])
self.set_parameters(["epoch", "pop_size", "neighbour_size"])
self.sort_flag = False
[docs] def evolve(self, epoch):
"""
Args:
epoch (int): The current iteration
"""
ranks = np.array(list(range(1, self.pop_size + 1)))
ranks = ranks / np.sum(ranks)
step_size = np.mean(self.problem.ub - self.problem.lb) * np.exp(-2 * (epoch + 1) / self.epoch)
ss = step_size * ranks
pop = []
for idx in range(0, self.pop_size):
pop_neighbours = []
for jdx in range(0, self.neighbour_size):
pos_new = self.pop[idx].solution + self.generator.normal(0, 1, self.problem.n_dims) * ss[idx]
pos_new = self.correct_solution(pos_new)
agent = self.generate_empty_agent(pos_new)
pop_neighbours.append(agent)
if self.mode not in self.AVAILABLE_MODES:
pop_neighbours[-1].target = self.get_target(pos_new)
pop_neighbours = self.update_target_for_population(pop_neighbours)
best_local = self.get_best_agent(pop_neighbours, self.problem.minmax)
pop.append(best_local)
if self.mode not in self.AVAILABLE_MODES:
self.pop[idx] = self.get_better_agent(best_local, self.pop[idx], self.problem.minmax)
if self.mode in self.AVAILABLE_MODES:
self.pop = self.greedy_selection_population(self.pop, pop, self.problem.minmax)