基于python的乌鸦搜索算法复现 Crow Search Algorithm(CSA)

import random # random Function import numpy as np # numpy operations import matplotlib.pyplot as plt import math # ceil function import test_function class CSA(): def __init__(self, pop_size=5, n_dim=2, ap=0.1, lb=-1e5, ub=1e5, max_iter=20, func=None): self.pop = pop_size self.n_dim = n_dim self.ap = ap # 感知概率 self.func = func self.max_iter = max_iter # max iter self.fly_length = [2 for _ in range(self.n_dim)] # 飞行距离，可以考虑是否采用莱维飞行或者随迭代次数改变 # 或许也和变量维数相关 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): num = np.array([random.randint(0, self.pop - 1) for _ in range(self.pop)]) # Generation of random candidate crows for following (chasing) for i in range(self.pop): if (random.random() > self.ap): for j in range(self.n_dim): self.X[(i, j)] = self.X[(i, j)] + self.fly_length[j] * ((random.random()) * (self.pbest_x[(num[i], j)] - self.X[(i, j)])) else: for j in range(self.n_dim): # 随机生成或许可以考虑采用莱维飞行 self.X[(i, j)] = self.lb[j] - (self.lb[j] - self.ub[j]) * random.random() 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.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__': # todo(xionglei@sjtu.edu.cn): 哪里有问题，复现的寻优效果很差 n_dim = 30 lb = [-100 for i in range(n_dim)] ub = [100 for i in range(n_dim)] demo_func = test_function.fu2 pop_size = 100 max_iter = 1000 csa = CSA(n_dim=n_dim, pop_size=pop_size, max_iter=max_iter, lb=lb, ub=ub, func=demo_func) best_x, bext_y = csa.run() print(f'{demo_func(csa.gbest_x)}\t{csa.gbest_x}') plt.plot(csa.gbest_y_hist) plt.show()