粒子群算法python代码

下面是一个简单的粒子群优化算法的 Python 代码示例：

import random

class Particle:
    def __init__(self, dimensions, bounds):
        self.position = [random.uniform(bounds[i][0], bounds[i][1]) for i in range(dimensions)]
        self.velocity = [random.uniform(-1, 1) for _ in range(dimensions)]
        self.best_position = self.position.copy()
        self.best_fitness = float('inf')

    def update_velocity(self, global_best_position, inertia_weight, cognitive_weight, social_weight):
        for i in range(len(self.velocity)):
            r1 = random.random()
            r2 = random.random()
            cognitive = cognitive_weight * r1 * (self.best_position[i] - self.position[i])
            social = social_weight * r2 * (global_best_position[i] - self.position[i])
            self.velocity[i] = inertia_weight * self.velocity[i] + cognitive + social

    def update_position(self, bounds):
        for i in range(len(self.position)):
            self.position[i] += self.velocity[i]
            if self.position[i] < bounds[i][0]:
                self.position[i] = bounds[i][0]
            elif self.position[i] > bounds[i][1]:
                self.position[i] = bounds[i][1]

    def evaluate_fitness(self, objective_function):
        fitness = objective_function(self.position)
        if fitness < self.best_fitness:
            self.best_fitness = fitness
            self.best_position = self.position.copy()

def particle_swarm_optimization(objective_function, dimensions, bounds, num_particles, max_iterations, inertia_weight, cognitive_weight, social_weight):
    particles = [Particle(dimensions, bounds) for _ in range(num_particles)]
    global_best_fitness = float('inf')
    global_best_position = None

    for _ in range(max_iterations):
        for particle in particles:
            particle.update_velocity(global_best_position, inertia_weight, cognitive_weight, social_weight)
            particle.update_position(bounds)
            particle.evaluate_fitness(objective_function)

            if particle.best_fitness < global_best_fitness:
                global_best_fitness = particle.best_fitness
                global_best_position = particle.best_position.copy()

    return global_best_position, global_best_fitness

# 示例目标函数：求解 f(x) = x^2 的最小值
def objective_function(x):
    return x[0] ** 2

# 算法参数设置
dimensions = 1
bounds = [(-10, 10)] # 变量的取值范围
num_particles = 20
max_iterations = 100
inertia_weight = 0.5
cognitive_weight = 1.0
social_weight = 1.0

# 运行粒子群优化算法
best_position, best_fitness = particle_swarm_optimization(objective_function, dimensions, bounds, num_particles, max_iterations, inertia_weight, cognitive_weight, social_weight)

print("Best position:", best_position)
print("Best fitness:", best_fitness)

请注意，这只是一个简单示例，可能不适用于所有情况。你可以根据自己的需求进行修改和优化。