# ！usr/bin/env python
# -*- coding: utf-8 -*-
# Time : 2021/12/3 17:36
# @Author : LucXiong
# @Project : Model
# @File : SBO.py
"""
Ref:https://blog.csdn.net/u011835903/article/details/107857884
"""
import random # random Function
import numpy as np # numpy operations
import matplotlib.pyplot as plt
import math
import test_function
class SBO():
def __init__(self, pop_size=50, n_dim=2, alpha=0.94, lb=-1e5, ub=1e5, max_iter=1000, func=None):
self.pop = pop_size
self.n_dim = n_dim
self.alpha = alpha # 步长的最大阈值
self.func = func
self.max_iter = max_iter # max iter
self.z = 0.02 # z是缩放比例因子
self.r_mutate = 0.05 # 变异概率
self.lb, self.ub = np.array(lb) * np.ones(self.n_dim), np.array(ub) * np.ones(self.n_dim)
assert self.n_dim == len(self.lb) == len(self.ub), 'dim == len(lb) == len(ub) is not True'
assert np.all(self.ub > self.lb), 'upper-bound must be greater than lower-bound'
self.X = np.random.uniform(low=self.lb, high=self.ub, size=(self.pop, self.n_dim))
self.Y = [self.func(self.X[i]) for i in range(len(self.X))] # y = f(x) for all particles
self.pbest_x = self.X.copy() # personal best location of every particle in history
self.pbest_y = [np.inf for i in range(self.pop)] # best image of every particle in history
self.fit = [1 / (1 + self.Y[i]) if self.Y[i] > 0 else 1 - self.Y[i] for i in range(self.pop)]
self.prob = [self.fit[i] / sum(self.fit) for i in range(self.pop)]
self.gbest_x = self.pbest_x.mean(axis=0).reshape(1, -1) # global best location for all particles
self.gbest_y = np.inf # global best y for all particles
self.gbest_y_hist = [] # gbest_y of every iteration
self.update_gbest()
def update_pbest(self):
'''
personal best
:return:
'''
for i in range(len(self.Y)):
if self.pbest_y[i] > self.Y[i]:
self.pbest_x[i] = self.X[i]
self.pbest_y[i] = self.Y[i]
def update_gbest(self):
'''
global best
:return:
'''
idx_min = self.pbest_y.index(min(self.pbest_y))
if self.gbest_y > self.pbest_y[idx_min]:
self.gbest_x = self.X[idx_min, :].copy()
self.gbest_y = self.pbest_y[idx_min]
def cal_prob(self):
self.fit = [1 / (1 + self.Y[i]) if self.Y[i] > 0 else 1 - self.Y[i] for i in range(self.pop)]
self.prob = [self.fit[i] / sum(self.fit) for i in range(self.pop)]
def update(self, iter_num):
idx_min = self.Y.index(min(self.Y))
for i in range(self.pop):
# roulette wheel
for k in range(self.n_dim):
select_list = []
while len(select_list) < 1:
select_list = []
r = np.random.rand(1)
for kk in range(len(self.prob)):
if self.prob[kk] > (r[0]):
select_list.append(kk)
j = random.choice(select_list)
lemta = self.alpha / (1 + self.prob[j])
try:
self.X[i, k] += lemta * 0.5 * (self.X[j, k] + self.gbest_x[0][k] - 2 * self.X[i, k])
except:
self.X[i, k] += lemta * 0.5 * (self.X[j, k] + self.gbest_x[k] - 2 * self.X[i, k])
# 变异
if np.random.rand(1) < self.r_mutate and i != idx_min:
for j in range(self.n_dim):
# 正态分布 Satin bowerbird optimizer: A new optimization algorithm to optimize
# ANFIS for software development effort estimation.pdf
self.X[i][j] += self.z * np.random.normal() * (self.ub[j] - self.lb[j])
self.X = np.clip(self.X, self.lb, self.ub)
self.Y = [self.func(self.X[i]) for i in range(len(self.X))]
# def mutate(self):
# # 没有用上这个函数
# idx_min = self.Y.index(min(self.Y))
# for i in range(self.pop):
# if np.random.rand(1)[0] < self.r_mutate and i != idx_min:
# for j in range(self.n_dim):
# self.X[i][j] += self.z * np.random.normal() * (self.ub[j] - self.lb[j])
# self.X = np.clip(self.X, self.lb, self.ub)
# self.Y = [self.func(self.X[i]) for i in range(len(self.X))]
def run(self):
for i in range(self.max_iter):
print(i)
self.update(i)
self.cal_prob()
self.update_pbest()
self.update_gbest()
self.gbest_y_hist.append(self.gbest_y)
self.best_x, self.best_y = self.gbest_x, self.gbest_y
return self.best_x, self.best_y
if __name__ == '__main__':
# 寻优效果不错，但是计算时间较长，有一部分原因是我频繁整体计算适应度值
n_dim = 30
lb = [-100 for i in range(n_dim)]
ub = [100 for i in range(n_dim)]
demo_func = test_function.fu5
pop_size = 100
max_iter = 100
sbo = SBO(n_dim=n_dim, pop_size=pop_size, max_iter=max_iter, lb=lb, ub=ub, func=demo_func)
best_x, bext_y = sbo.run()
print(f'{demo_func(sbo.gbest_x)}\t{sbo.gbest_x}')
plt.plot(sbo.gbest_y_hist)
plt.show()
