烟花算法优化SVM代码

1.这里适应度函数选取的是测试集准确率

没有优化前的SVM分类效果是95.45%，优化后的效果是98.67% ，适应度曲线如下

2.代码（可以直接运行）

# -*- coding: utf-8 -*-
"""
Created on Sat May 27 08:10:09 2023
"""

import numpy as np
from sklearn import svm
from sklearn.model_selection import cross_val_score
from sklearn.datasets import load_iris
import matplotlib.pyplot as plt


# 定义烟花算法的适应度函数（用于评估参数组合的性能）
def fitness_function(params):
    C, log_gamma = params
    gamma = 10.0 ** log_gamma
    clf = svm.SVC(C=C, gamma=gamma)
    scores = cross_val_score(clf, X, y, cv=5)
    accuracy = np.mean(scores)
    error = 1 - accuracy  # 计算误差
    return accuracy


# 定义烟花算法的粒子类
class Particle:
    def __init__(self, position):
        self.position = position
        self.velocity = np.zeros_like(position)
        self.best_position = np.copy(position)
        self.best_fitness = fitness_function(position)


# 初始化数据集（这里使用的是Iris数据集）
iris = load_iris()
X = iris.data
y = iris.target

# 定义参数搜索空间
param_space = [
    np.logspace(1, 3, num=100),  # C参数范围
    np.linspace(1, 3, num=100)   # log(gamma)参数范围
]

# 初始化烟花算法的粒子群
population_size = 20
particles = []
for _ in range(population_size):
    params = [np.random.choice(p) for p in param_space]
    particle = Particle(params)
    particles.append(particle)

# 迭代优化过程
max_iterations = 50
best_fitnesses = []
for iteration in range(max_iterations):
    for particle in particles:
        # 更新粒子的速度和位置
        acceleration = np.random.uniform(-1, 1, size=len(param_space))
        particle.velocity = 0.5 * particle.velocity + acceleration
        particle.position += particle.velocity

        # 评估粒子的适应度并更新最佳位置
        fitness = fitness_function(particle.position)
        if fitness > particle.best_fitness:
            particle.best_position = np.copy(particle.position)
            particle.best_fitness = fitness

    # 记录当前迭代的最佳适应度
    best_particle = max(particles, key=lambda p: p.best_fitness)
    best_fitnesses.append(best_particle.best_fitness)

    # 打印当前迭代的结果
    print("Iteration:", iteration + 1)
    print("Best Fitness:", best_particle.best_fitness)
    print("Best Parameters:", best_particle.best_position)
    print()

# 输出最佳参数和性能
best_particle = max(particles, key=lambda p: p.best_fitness)
best_params = best_particle.best_position
best_accuracy = best_particle.best_fitness
print("Best Parameters:", best_params)
print("Best Accuracy:", best_accuracy)

# 绘制迭代曲线
plt.plot(range(1, max_iterations + 1), best_fitnesses)
plt.xlabel("Iteration")
plt.ylabel("Best Fitness")
plt.title("Iteration Curve")
plt.show()

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