粒子群算法无功优化python

以下是一个简单的粒子群算法无功优化的Python代码示例：

import random

# 功率因数目标函数
def power_factor(q):
    return 1 / (1 + abs(q))

# 粒子群算法
class ParticleSwarmOptimization:
    def __init__(self, num_particles, max_iterations, c1, c2, w, qmin, qmax):
        self.num_particles = num_particles  # 粒子数
        self.max_iterations = max_iterations  # 最大迭代次数
        self.c1 = c1  # 加速常数1
        self.c2 = c2  # 加速常数2
        self.w = w  # 惯性权重
        self.qmin = qmin  # 无功最小值
        self.qmax = qmax  # 无功最大值
        self.gbest = None  # 全局最优解
        self.particles = []  # 所有粒子
        for i in range(num_particles):
            q = random.uniform(qmin, qmax)  # 初始化无功
            particle = {'q': q, 'v': 0, 'pbest': None}  # 粒子数据结构
            particle['pbest'] = particle  # 初始个体最优解
            self.particles.append(particle)

    def optimize(self):
        for i in range(self.max_iterations):
            for particle in self.particles:
                # 更新速度和位置
                particle['v'] = self.w * particle['v'] + \
                    self.c1 * random.random() * (particle['pbest']['q'] - particle['q']) + \
                    self.c2 * random.random() * (self.gbest['q'] - particle['q'])
                particle['q'] += particle['v']
                # 限制无功范围
                if particle['q'] < self.qmin:
                    particle['q'] = self.qmin
                elif particle['q'] > self.qmax:
                    particle['q'] = self.qmax
                # 更新个体最优解
                if power_factor(particle['q']) > power_factor(particle['pbest']['q']):
                    particle['pbest'] = particle
                # 更新全局最优解
                if self.gbest is None or power_factor(particle['q']) > power_factor(self.gbest['q']):
                    self.gbest = particle
            # 输出每次迭代的全局最优解
            print('Iteration {}: {}'.format(i+1, self.gbest['q']))

# 测试
pso = ParticleSwarmOptimization(num_particles=20, max_iterations=100, c1=2, c2=2, w=0.7, qmin=-10, qmax=10)
pso.optimize()
print('Optimal Q: ', pso.gbest['q'])
print('Optimal Power Factor: ', power_factor(pso.gbest['q']))

在这个例子中，我们定义了一个 `power_factor` 函数用于计算功率因数，然后实现了一个 `ParticleSwarmOptimization` 类来执行粒子群算法。在 `optimize` 方法中，我们对每个粒子进行速度和位置更新，并根据新的位置更新个体最优解和全局最优解。最终，我们输出了找到的最佳无功值和相应的功率因数。