python 粒子羣算法（PSO）Particle Swarm Optimization

Particle Swarm Optimization

# python-Particle Swarm Optimization
# coding=utf-8
import numpy as np
import matplotlib.pyplot as plt

def Schwefel(X):
    f = 418.9829 * len(X)
    for i in X:
        f -= i * np.sin(np.sqrt(np.abs(i)))
    return f

class Particle:
    def __init__(self, domain_position, domain_velocity, dim):
        self.pos = [np.random.uniform(-domain_position, domain_position) for i in range(dim)]
        self.vel = [np.random.uniform(-domain_velocity, domain_velocity) for i in range(dim)]
        self.solution_position = [0.0 for i in range(dim)]
        self.local_minimum = Schwefel(self.pos)

class PSO:
    def __init__(self, dim, size, domain_position, domain_velocity, C1=2, C2=2, weight=1):
        self.dim = dim
        self.size = size
        self.domain_position = domain_position
        self.domain_velocity = domain_velocity
        self.C1 = C1
        self.C2 = C2
        self.weight = weight
        self.global_minimum = float('INF')
        self.solution_position = [0.0 for i in range(dim)]
        self.ParticleSwarm = [Particle(self.domain_position, self.domain_velocity, self.dim) for i in range(self.size)]
        self.global_loss = []

    def fit(self, epochs):
        for i in range(epochs):
            for particle in self.ParticleSwarm:
                self.update_velocity(particle)
                self.update_position(particle)
            self.global_loss.append(self.global_minimum)

    def update_velocity(self, particle):
        for i in range(self.dim):
            val_temp = self.weight * particle.vel[i] + self.C1 * np.random.normal(0, 1) * (particle.solution_position[i] - particle.pos[i]) + self.C2 * np.random.normal(0, 1) * (self.solution_position[i] - particle.pos[i])
            if val_temp > self.domain_velocity:
                val_temp = self.domain_velocity
            elif val_temp < -self.domain_velocity:
                val_temp = -self.domain_velocity
            particle.vel[i] = val_temp

    def update_position(self, particle):
        for i in range(self.dim):
            particle.pos[i] = particle.pos[i] + particle.vel[i]
            local_minimum = Schwefel(particle.pos)
        if local_minimum < particle.local_minimum:
            particle.local_minimum = local_minimum
            for i in range(self.dim):
                particle.solution_position[i] = particle.pos[i]
        if local_minimum < self.global_minimum:
            self.global_minimum = local_minimum
            for i in range(self.dim):
                self.solution_position[i] = particle.pos[i]

if __name__ == "__main__":
    np.random.seed(8)
    dim = 2
    size = 10
    epochs = 1000
    domain_position = 500
    domain_velocity = 0.15
    model = PSO(dim, size, domain_position, domain_velocity)
    model.fit(epochs)
    print("Solution position:", model.solution_position)
    print("Global minimum: %f" % Schwefel(model.solution_position))
    plt.plot(model.global_loss, "-r", linewidth=2, label='loss')
    plt.legend()
    plt.grid(True)
    plt.show()