#!/usr/bin/env python
# Created by "Thieu" at 17:48, 18/03/2020 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import numpy as np
from mealpy.optimizer import Optimizer
[docs]class DevHS(Optimizer):
"""
The developed version: Harmony Search (HS)
Links:
1. https://doi.org/10.1177/003754970107600201
Notes:
- Used the global best in the harmony memories
- Removed all third for loops
Hyper-parameters should fine-tune in approximate range to get faster convergence toward the global optimum:
+ c_r (float): [0.1, 0.5], Harmony Memory Consideration Rate), default = 0.15
+ pa_r (float): [0.3, 0.8], Pitch Adjustment Rate, default=0.5
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, HS
>>>
>>> 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 = HS.DevHS(epoch=1000, pop_size=50, c_r = 0.95, pa_r = 0.05)
>>> 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: int = 10000, pop_size: int = 100, c_r: float = 0.95, pa_r: float = 0.05, **kwargs: object) -> None:
"""
Args:
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size, default = 100
c_r (float): Harmony Memory Consideration Rate, default = 0.15
pa_r (float): Pitch Adjustment Rate, default=0.5
"""
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.c_r = self.validator.check_float("c_r", c_r, (0, 1.0))
self.pa_r = self.validator.check_float("pa_r", pa_r, (0, 1.0))
self.set_parameters(["epoch", "pop_size", "c_r", "pa_r"])
self.sort_flag = False
[docs] def initialize_variables(self):
self.fw = 0.0001 * (self.problem.ub - self.problem.lb) # Fret Width (Bandwidth)
self.fw_damp = 0.9995 # Fret Width Damp Ratio
self.dyn_fw = self.fw
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
pop_new = []
for idx in range(0, self.pop_size):
# Create New Harmony Position
pos_new = self.generator.uniform(self.problem.lb, self.problem.ub)
delta = self.dyn_fw * self.generator.normal(self.problem.lb, self.problem.ub)
# Use Harmony Memory
pos_new = np.where(self.generator.random(self.problem.n_dims) < self.c_r, self.g_best.solution, pos_new)
# Pitch Adjustment
x_new = pos_new + delta
pos_new = np.where(self.generator.random(self.problem.n_dims) < self.pa_r, x_new, pos_new)
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)
pop_new = self.update_target_for_population(pop_new)
# Update Damp Fret Width
self.dyn_fw = self.dyn_fw * self.fw_damp
# Merge Harmony Memory and New Harmonies, Then sort them, Then truncate extra harmonies
self.pop = self.get_sorted_and_trimmed_population(self.pop + pop_new, self.pop_size, minmax=self.problem.minmax)
[docs]class OriginalHS(DevHS):
"""
The original version of: Harmony Search (HS)
Links:
1. https://doi.org/10.1177/003754970107600201
Hyper-parameters should fine-tune in approximate range to get faster convergence toward the global optimum:
+ c_r (float): [0.1, 0.5], Harmony Memory Consideration Rate), default = 0.15
+ pa_r (float): [0.3, 0.8], Pitch Adjustment Rate, default=0.5
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, HS
>>>
>>> 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 = HS.OriginalHS(epoch=1000, pop_size=50, c_r = 0.95, pa_r = 0.05)
>>> 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] Geem, Z.W., Kim, J.H. and Loganathan, G.V., 2001. A new heuristic
optimization algorithm: harmony search. simulation, 76(2), pp.60-68.
"""
def __init__(self, epoch: int = 10000, pop_size: int = 100, c_r: float = 0.95, pa_r: float = 0.05, **kwargs: object) -> None:
"""
Args:
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size, default = 100
c_r (float): Harmony Memory Consideration Rate), default = 0.15
pa_r (float): Pitch Adjustment Rate, default=0.5
"""
super().__init__(epoch, pop_size, c_r, pa_r, **kwargs)
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
pop_new = []
for idx in range(0, self.pop_size):
pos_new = self.generator.uniform(self.problem.lb, self.problem.ub)
for jdx in range(self.problem.n_dims):
# Use Harmony Memory
if self.generator.uniform() <= self.c_r:
random_index = self.generator.integers(0, self.pop_size)
pos_new[jdx] = self.pop[random_index].solution[jdx]
# Pitch Adjustment
if self.generator.uniform() <= self.pa_r:
delta = self.dyn_fw * self.generator.normal(self.problem.lb, self.problem.ub) # Gaussian(Normal)
pos_new[jdx] = pos_new[jdx] + delta[jdx]
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)
pop_new = self.update_target_for_population(pop_new)
# Update Damp Fret Width
self.dyn_fw = self.dyn_fw * self.fw_damp
# Merge Harmony Memory and New Harmonies, Then sort them, Then truncate extra harmonies
self.pop = self.get_sorted_and_trimmed_population(self.pop + pop_new, self.pop_size, minmax=self.problem.minmax)