灰狼优化算法结合聚类的程序代码

以下是一个简单的灰狼优化算法结合聚类的程序代码示例：

import numpy as np
from sklearn.cluster import KMeans

# 灰狼优化算法
class GreyWolfOptimizer:
    def __init__(self, obj_func, dim, num_wolves=10, lb=-100, ub=100, max_iter=100):
        self.obj_func = obj_func
        self.dim = dim
        self.num_wolves = num_wolves
        self.lb = lb
        self.ub = ub
        self.max_iter = max_iter
        self.alpha_pos = None
        self.beta_pos = None
        self.delta_pos = None

    def init_wolves(self):
        self.alpha_pos = np.zeros(self.dim)
        self.beta_pos = np.zeros(self.dim)
        self.delta_pos = np.zeros(self.dim)
        self.alpha_score = float("inf")
        self.beta_score = float("inf")
        self.delta_score = float("inf")
        self.wolves = np.random.uniform(self.lb, self.ub, size=(self.num_wolves, self.dim))

    def update_wolves(self, iter):
        a = 2 * (1 - iter / self.max_iter)  # a decreases linearly from 2 to 0
        for i in range(self.num_wolves):
            for j in range(self.dim):
                r1 = np.random.random()  # r1 is a random number in [0,1]
                r2 = np.random.random()  # r2 is a random number in [0,1]
                A1 = 2 * a * r1 - a  # Equation (3.3)
                C1 = 2 * r2  # Equation (3.4)
                D_alpha = abs(C1 * self.alpha_pos[j] - self.wolves[i, j])  # Equation (3.5)-part 1
                X1 = self.alpha_pos[j] - A1 * D_alpha  # Equation (3.6)-part 1
                r1 = np.random.random()
                r2 = np.random.random()
                A2 = 2 * a * r1 - a  # Equation (3.3)
                C2 = 2 * r2  # Equation (3.4)
                D_beta = abs(C2 * self.beta_pos[j] - self.wolves[i, j])  # Equation (3.5)-part 2
                X2 = self.beta_pos[j] - A2 * D_beta  # Equation (3.6)-part 2
                r1 = np.random.random()
                r2 = np.random.random()
                A3 = 2 * a * r1 - a  # Equation (3.3)
                C3 = 2 * r2  # Equation (3.4)
                D_delta = abs(C3 * self.delta_pos[j] - self.wolves[i, j])  # Equation (3.5)-part 3
                X3 = self.delta_pos[j] - A3 * D_delta  # Equation (3.5)-part 3
                self.wolves[i, j] = (X1 + X2 + X3) / 3  # Equation (3.7)

    def update_alpha_beta_delta(self):
        scores = [self.obj_func(wolf) for wolf in self.wolves]
        self.alpha_score = min(scores)
        self.alpha_pos = self.wolves[np.argmin(scores)]
        if self.alpha_score < self.beta_score:
            self.beta_score = self.alpha_score
            self.beta_pos = self.alpha_pos.copy()
        if self.alpha_score < self.delta_score:
            self.delta_score = self.beta_score
            self.delta_pos = self.beta_pos.copy()

    def optimize(self):
        self.init_wolves()
        for i in range(self.max_iter):
            self.update_wolves(i)
            self.update_alpha_beta_delta()
        return self.alpha_pos, self.alpha_score

# 灰狼优化算法结合聚类
class GWOClustering:
    def __init__(self, data, num_clusters, max_iter=100):
        self.data = data
        self.num_clusters = num_clusters
        self.max_iter = max_iter

    def obj_func(self, wolves):
        kmeans = KMeans(n_clusters=self.num_clusters, init=wolves)
        kmeans.fit(self.data)
        return kmeans.inertia_

    def run(self):
        gwo = GreyWolfOptimizer(self.obj_func, self.data.shape[1], max_iter=self.max_iter)
        wolves = np.random.uniform(0, 1, size=(gwo.num_wolves, self.data.shape[1]))
        wolves *= (self.data.max(axis=0) - self.data.min(axis=0))
        wolves += self.data.min(axis=0)
        gwo.wolves = wolves
        best_pos, best_score = gwo.optimize()
        kmeans = KMeans(n_clusters=self.num_clusters, init=best_pos)
        kmeans.fit(self.data)
        return kmeans.labels_

使用示例：

from sklearn.datasets import make_blobs

data, _ = make_blobs(n_samples=1000, centers=3, random_state=42)

gwo_clustering = GWOClustering(data, num_clusters=3)
labels = gwo_clustering.run()

print(labels)

这个示例代码使用 `make_blobs` 生成了一组测试数据，然后使用 `GWOClustering` 类进行聚类。在 `GWOClustering` 类中，我们将灰狼优化算法的目标函数设置为 KMeans 算法的 SSE 值，并使用 `GreyWolfOptimizer` 类进行优化。最终返回聚类标签。