import numpy as np
import random
import matplotlib.pyplot as plt
import test_function
class wps():
def __init__(self, n_dim=2, pop_size=50, max_iter=150, lb=-1e5, ub=1e5, step=0.5, func=None):
# D - dimension of the search space;
# N - number of wolves to generate;
# step, l_near, t_max, beta - model parameters;
# fitness_function - function to optimize;
# w_range - range of particles' coordinates' values (from -range to range);
# iter_num - maximum number of iterations.
# (self, n_dim, pop_size, step, fitness_function, w_range, iter_num, l_near=0, t_max=0, beta=0):
self.n_dim = n_dim
self.pop = pop_size
self.step = step # 影响朝最佳点靠近的速度
self.func = func
self.max_iter = max_iter
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.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 update(self):
# 参考樽海鞘群算法
# self.step = 2 * math.exp(-(4 * ((i + 1) / self.max_iter)) ** 2)
for i in range(self.pop):
if all(self.X[i] != self.gbest_x[0]): # self.Y[i] != self.gbest_y
try:
self.X[i] += self.step * (self.gbest_x[0] - self.X[i]) / np.linalg.norm(self.gbest_x[0] - self.X[i])
except:
self.X[i] += self.step * (self.gbest_x - self.X[i]) / np.linalg.norm(self.gbest_x - self.X[i])
self.X = np.clip(self.X, self.lb, self.ub)
self.Y = [self.func(self.X[i]) for i in range(len(self.X))] # Function for fitness evaluation of new solutions
def run(self):
for iter in range(self.max_iter):
self.update() # self.step可以选择参考樽海鞘群，随迭代次数发生改变
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__':
n_dim = 2
lb = [-5.12 for i in range(n_dim)]
ub = [5.12 for i in range(n_dim)]
demo_func = test_function.f22
ssa = wps(pop_size=50, n_dim=n_dim, max_iter=300, lb=lb, ub=ub, func=demo_func, step=0.5)
ssa.run()
print('best_x is ', ssa.gbest_x, 'best_y is', ssa.gbest_y)
print(f'{demo_func(ssa.gbest_x)}\t{ssa.gbest_x}')
plt.plot(ssa.gbest_y_hist)
plt.show()
