基于Python的模拟退火算法SA 以函数极值+TSP问题为例（gif动态展示）

from abc import ABCMeta, abstractmethod
import types


class SkoBase(metaclass=ABCMeta):
    def register(self, operator_name, operator, *args, **kwargs):
        ‘‘‘
        regeister udf to the class
        :param operator_name: string
        :param operator: a function, operator itself
        :param args: arg of operator
        :param kwargs: kwargs of operator
        :return:
        ‘‘‘

        def operator_wapper(*wrapper_args):
            return operator(*(wrapper_args + args), **kwargs)

        setattr(self, operator_name, types.MethodType(operator_wapper, self))
        return self

# 空函数
class Problem(object):
    pass

import numpy as np


def mutation(self):
    ‘‘‘
    mutation of 0/1 type chromosome
    faster than `self.Chrom = (mask + self.Chrom) % 2`
    :param self:
    :return:
    ‘‘‘
    #
    mask = (np.random.rand(self.size_pop, self.len_chrom)  self.prob_mut)
    self.Chrom ^= mask
    return self.Chrom


def mutation_TSP_1(self):
    ‘‘‘
    every gene in every chromosome mutate
    :param self:
    :return:
    ‘‘‘
    for i in range(self.size_pop):
        for j in range(self.n_dim):
            if np.random.rand()  self.prob_mut:
                n = np.random.randint(0, self.len_chrom, 1)
                self.Chrom[i, j], self.Chrom[i, n] = self.Chrom[i, n], self.Chrom[i, j]
    return self.Chrom


# 交换两个点
def swap(individual):
    n1, n2 = np.random.randint(0, individual.shape[0] - 1, 2)
    if n1 >= n2:
        n1, n2 = n2, n1 + 1
    individual[n1], individual[n2] = individual[n2], individual[n1]
    return individual

# 逆转序列中指定的一段
def reverse(individual):
    ‘‘‘
    Reverse n1 to n2
    Also called `2-Opt`: removes two random edges, reconnecting them so they cross
    Karan Bhatia, "Genetic Algorithms and the Traveling Salesman Problem", 1994
    https://pdfs.semanticscholar.org/c5dd/3d8e97202f07f2e337a791c3bf81cd0bbb13.pdf
    ‘‘‘
    n1, n2 = np.random.randint(0, individual.shape[0] - 1, 2)
    if n1 >= n2:
        n1, n2 = n2, n1 + 1
    individual[n1:n2] = individual[n1:n2][::-1]
    return individual

# 分段交叉
def transpose(individual):
    # randomly generate n1 
    n1, n2, n3 = sorted(np.random.randint(0, individual.shape[0] - 2, 3))
    n2 += 1
    n3 += 2
    slice1, slice2, slice3, slice4 = individual[0:n1], individual[n1:n2], individual[n2:n3 + 1], individual[n3 + 1:]
    individual = np.concatenate([slice1, slice3, slice2, slice4])
    return individual


def mutation_reverse(self):
    ‘‘‘
    Reverse
    :param self:
    :return:
    ‘‘‘
    for i in range(self.size_pop):
        if np.random.rand()  self.prob_mut:
            self.Chrom[i] = reverse(self.Chrom[i])
    return self.Chrom


def mutation_swap(self):
    for i in range(self.size_pop):
        if np.random.rand()  self.prob_mut:
            self.Chrom[i] = swap(self.Chrom[i])
    return self.Chrom

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# @Time    : 2019/8/17
# @Author  : github.com/guofei9987

import numpy as np
from base import SkoBase
from sko.operators import mutation


class SimulatedAnnealingBase(SkoBase):
    """
    DO SA(Simulated Annealing)

    Parameters
    ----------------
    func : function
        The func you want to do optimal
    n_dim : int
        number of variables of func
    x0 : array, shape is n_dim
        initial solution
    T_max :float
        initial temperature
    T_min : float
        end temperature
    L : int
        num of iteration under every temperature（Long of Chain）

    Attributes
    ----------------------


    Examples
    -------------
    See https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_sa.py
    """
#L是单次迭代中马尔科夫链的长度
    def __init__(self, func, x0, T_max=100, T_min=1e-7, L=300, max_stay_counter=150, **kwargs):
        assert T_max > T_min > 0, ‘T_max > T_min > 0‘

        self.func = func
        self.T_max = T_max  # initial temperature
        self.T_min = T_min  # end temperature
        self.L = int(L)  # num of iteration under every temperature（also called Long of Chain）
        self.max_stay_counter = max_stay_counter  # stop if best_y stay unchanged over max_stay_counter times

        self.n_dims = len(x0)

        self.best_x = np.array(x0)  # initial solution
        self.best_y = self.func(self.best_x)
        self.T = self.T_max
        self.iter_cycle = 0
        self.best_y_history = [self.best_y]
        self.best_x_history = [self.best_x]

    def get_new_x(self, x):
        u = np.random.uniform(-1, 1, size=self.n_dims)#产生[-1,1]之间的随机数，维数指定
        x_new = x + 20 * np.sign(u) * self.T * ((1 + 1.0 / self.T) ** np.abs(u) - 1.0)
        return x_new

    def cool_down(self):
        self.T = self.T * 0.7

    def isclose(self, a, b, rel_tol=1e-09, abs_tol=1e-30):
        return abs(a - b)  max(abs(a), abs(b)), abs_tol)

    def run(self):
        x_current, y_current = self.best_x, self.best_y
        stay_counter = 0
        while True:
            for i in range(self.L):
                x_new = self.get_new_x(x_current)
                y_new = self.func(x_new)

                # Metropolis
                df = y_new - y_current
                if df or np.exp(-df / self.T) > np.random.rand():
                    x_current, y_current = x_new, y_new
                    if y_new  self.best_y:
                        self.best_x, self.best_y = x_new, y_new

            self.iter_cycle += 1
            self.cool_down()
            self.best_y_history.append(self.best_y)
            self.best_x_history.append(self.best_x)

            # if best_y stay for max_stay_counter times, stop iteration
            # 最后一次的best_y值和倒数第二次的best_y值一样，表示停留在平坦的状态
            if self.isclose(self.best_y_history[-1], self.best_y_history[-2]):
                stay_counter += 1
            else:
                stay_counter = 0

            if self.T  self.T_min:
                stop_code = ‘Cooled to final temperature‘
                break
            if stay_counter > self.max_stay_counter:
                stop_code = ‘Stay unchanged in the last {stay_counter} iterations‘.format(stay_counter=stay_counter)
                break

        return self.best_x, self.best_y

    fit = run


class SAFast(SimulatedAnnealingBase):
    ‘‘‘
    u ~ Uniform(0, 1, size = d)
    y = sgn(u - 0.5) * T * ((1 + 1/T)**abs(2*u - 1) - 1.0)

    xc = y * (upper - lower)
    x_new = x_old + xc

    c = n * exp(-n * quench)
    T_new = T0 * exp(-c * k**quench)
    ‘‘‘

    def __init__(self, func, x0, T_max=100, T_min=1e-7, L=300, max_stay_counter=150, **kwargs):
        super().__init__(func, x0, T_max, T_min, L, max_stay_counter, **kwargs)
        self.m, self.n, self.quench = kwargs.get(‘m‘, 1), kwargs.get(‘n‘, 1), kwargs.get(‘quench‘, 1)
        self.lower, self.upper = kwargs.get(‘lower‘, -10), kwargs.get(‘upper‘, 10)
        self.c = self.m * np.exp(-self.n * self.quench)

    def get_new_x(self, x):
        r = np.random.uniform(-1, 1, size=self.n_dims)
        xc = np.sign(r) * self.T * ((1 + 1.0 / self.T) ** np.abs(r) - 1.0)
        x_new = x + xc * (self.upper - self.lower)
        return x_new

    def cool_down(self):
        self.T = self.T_max * np.exp(-self.c * self.iter_cycle ** self.quench)


class SABoltzmann(SimulatedAnnealingBase):
    ‘‘‘
    std = minimum(sqrt(T) * ones(d), (upper - lower) / (3*learn_rate))
    y ~ Normal(0, std, size = d)
    x_new = x_old + learn_rate * y

    T_new = T0 / log(1 + k)
    ‘‘‘

    def __init__(self, func, x0, T_max=100, T_min=1e-7, L=300, max_stay_counter=150, **kwargs):
        super().__init__(func, x0, T_max, T_min, L, max_stay_counter, **kwargs)
        self.lower, self.upper = kwargs.get(‘lower‘, -10), kwargs.get(‘upper‘, 10)
        self.learn_rate = kwargs.get(‘learn_rate‘, 0.5)

    def get_new_x(self, x):
        std = min(np.sqrt(self.T), (self.upper - self.lower) / 3.0 / self.learn_rate) * np.ones(self.n_dims)
        xc = np.random.normal(0, 1.0, size=self.n_dims)
        x_new = x + xc * std * self.learn_rate
        return x_new

    def cool_down(self):
        self.T = self.T_max / np.log(self.iter_cycle + 1.0)


class SACauchy(SimulatedAnnealingBase):
    ‘‘‘
    u ~ Uniform(-pi/2, pi/2, size=d)
    xc = learn_rate * T * tan(u)
    x_new = x_old + xc

    T_new = T0 / (1 + k)
    ‘‘‘

    def __init__(self, func, x0, T_max=100, T_min=1e-7, L=300, max_stay_counter=150, **kwargs):
        super().__init__(func, x0, T_max, T_min, L, max_stay_counter, **kwargs)
        self.learn_rate = kwargs.get(‘learn_rate‘, 0.5)

    def get_new_x(self, x):
        u = np.random.uniform(-np.pi / 2, np.pi / 2, size=self.n_dims)
        xc = self.learn_rate * self.T * np.tan(u)
        x_new = x + xc
        return x_new

    def cool_down(self):
        self.T = self.T_max / (1 + self.iter_cycle)


# SA_fast is the default
SA = SAFast

class SA_TSP(SimulatedAnnealingBase):
    def cool_down(self):
        self.T = self.T_max / (1 + np.log(1 + self.iter_cycle))

    def get_new_x(self, x):
        x_new = x.copy()
        new_x_strategy = np.random.randint(3)
        if new_x_strategy == 0:
            x_new = mutation.swap(x_new)
        elif new_x_strategy == 1:
            x_new = mutation.reverse(x_new)
        elif new_x_strategy == 2:
            x_new = mutation.transpose(x_new)

        return x_new

import numpy as np
# demo_func = lambda x: x[0] ** 2 + (x[1] - 0.05) ** 2 + x[2] ** 2
# 震荡函数，有无穷多个极小值点
demo_func = lambda x: 0.5+(np.square(np.sin(np.sqrt(x[0]**2+x[1]**2)))-0.5)/np.square(1+0.001*(x[0]**2+x[1]**2))

# %% Do SA
from sko.SA import SA

sa = SA(func=demo_func, x0=[1,1], T_max=1, T_min=1e-9, L=300, max_stay_counter=150)
best_x, best_y = sa.run()
print(‘best_x:‘, best_x, ‘best_y‘, best_y)

# %% Plot the result
import matplotlib.pyplot as plt
import pandas as pd

plt.plot(pd.DataFrame(sa.best_y_history))
plt.show()

# %%
from sko.SA import SAFast

sa_fast = SAFast(func=demo_func, x0=[1, 1], T_max=1, T_min=1e-9, q=0.99, L=300, max_stay_counter=150)
sa_fast.run()
print(‘Fast Simulated Annealing: best_x is ‘, sa_fast.best_x, ‘best_y is ‘, sa_fast.best_y)

# %%
from sko.SA import SABoltzmann

sa_boltzmann = SABoltzmann(func=demo_func, x0=[1, 1], T_max=1, T_min=1e-9, q=0.99, L=300, max_stay_counter=150)
sa_boltzmann.run()
print(‘Boltzmann Simulated Annealing: best_x is ‘, sa_boltzmann.best_x, ‘best_y is ‘, sa_fast.best_y)

# %%
from sko.SA import SACauchy

sa_cauchy = SACauchy(func=demo_func, x0=[1, 1], T_max=1, T_min=1e-9, q=0.99, L=300, max_stay_counter=150)
sa_cauchy.run()
print(‘Cauchy Simulated Annealing: best_x is ‘, sa_cauchy.best_x, ‘best_y is ‘, sa_cauchy.best_y)

import numpy as np
from scipy import spatial
import matplotlib.pyplot as plt
import sys

file_name = sys.argv[1] if len(sys.argv) > 1 else ‘data/nctu.csv‘
points_coordinate = np.loadtxt(file_name, delimiter=‘,‘)
num_points = points_coordinate.shape[0]
distance_matrix = spatial.distance.cdist(points_coordinate, points_coordinate, metric=‘euclidean‘)
distance_matrix = distance_matrix * 111000  # 1 degree of lat/lon ~ = 111000m


def cal_total_distance(routine):
    ‘‘‘The objective function. input routine, return total distance.
    cal_total_distance(np.arange(num_points))
    ‘‘‘
    num_points, = routine.shape
    return sum([distance_matrix[routine[i % num_points], routine[(i + 1) % num_points]] for i in range(num_points)])


# %%
from sko.SA import SA_TSP

sa_tsp = SA_TSP(func=cal_total_distance, x0=range(num_points), T_max=100, T_min=1, L=10 * num_points)

best_points, best_distance = sa_tsp.run()
print(best_points, best_distance, cal_total_distance(best_points))
# %% Plot the best routine
from matplotlib.ticker import FormatStrFormatter

fig, ax = plt.subplots(1, 2)

best_points_ = np.concatenate([best_points, [best_points[0]]])
best_points_coordinate = points_coordinate[best_points_, :]
ax[0].plot(sa_tsp.best_y_history)
ax[0].set_xlabel("Iteration")
ax[0].set_ylabel("Distance")
ax[1].plot(best_points_coordinate[:, 0], best_points_coordinate[:, 1],
           marker=‘o‘, markerfacecolor=‘b‘, color=‘c‘, linestyle=‘-‘)
ax[1].xaxis.set_major_formatter(FormatStrFormatter(‘%.3f‘))
ax[1].yaxis.set_major_formatter(FormatStrFormatter(‘%.3f‘))
ax[1].set_xlabel("Longitude")
ax[1].set_ylabel("Latitude")
plt.show()

# %% Plot the animation
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation

best_x_history = sa_tsp.best_x_history

fig2, ax2 = plt.subplots(1, 1)
ax2.set_title(‘title‘, loc=‘center‘)
line = ax2.plot(points_coordinate[:, 0], points_coordinate[:, 1],
                marker=‘o‘, markerfacecolor=‘b‘, color=‘c‘, linestyle=‘-‘)
ax2.xaxis.set_major_formatter(FormatStrFormatter(‘%.3f‘))
ax2.yaxis.set_major_formatter(FormatStrFormatter(‘%.3f‘))
ax2.set_xlabel("Longitude")
ax2.set_ylabel("Latitude")
plt.ion()
p = plt.show()


def update_scatter(frame):
    ax2.set_title(‘iter = ‘ + str(frame))
    points = best_x_history[frame]
    points = np.concatenate([points, [points[0]]])
    point_coordinate = points_coordinate[points, :]
    plt.setp(line, ‘xdata‘, point_coordinate[:, 0], ‘ydata‘, point_coordinate[:, 1])
    return line


ani = FuncAnimation(fig2, update_scatter, blit=True, interval=25, frames=len(best_x_history))
plt.show()

# ani.save(‘sa_tsp.gif‘, writer=‘pillow‘)

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