python 自适应粒子群算法实现多目标优化代码

当然，我可以帮你提供一个 Python 的自适应粒子群算法实现多目标优化的代码示例。以下是一个简单的例子：

import random

class Particle:
    def __init__(self, position):
        self.position = position
        self.velocity = [random.uniform(-1, 1) for _ in range(len(position))]
        self.best_position = self.position.copy()

    def update_velocity(self, global_best_position, c1, c2):
        for i in range(len(self.velocity)):
            r1 = random.random()
            r2 = random.random()
            cognitive_component = c1 * r1 * (self.best_position[i] - self.position[i])
            social_component = c2 * r2 * (global_best_position[i] - self.position[i])
            self.velocity[i] += cognitive_component + social_component

    def update_position(self):
        self.position = [self.position[i] + self.velocity[i] for i in range(len(self.position))]

    def evaluate_fitness(self):
        # 根据具体问题定义适应度函数
        fitness1 = ...
        fitness2 = ...
        return [fitness1, fitness2]

def adaptive_pso(n_iterations, n_particles, n_dimensions, c1, c2):
    particles = [Particle([random.uniform(0, 1) for _ in range(n_dimensions)]) for _ in range(n_particles)]
    global_best_position = None

    for _ in range(n_iterations):
        for particle in particles:
            fitness = particle.evaluate_fitness()
            if not particle.best_position or fitness < particle.evaluate_fitness():
                particle.best_position = particle.position.copy()
                if not global_best_position or fitness < particle.evaluate_fitness():
                    global_best_position = particle.position.copy()

            particle.update_velocity(global_best_position, c1, c2)
            particle.update_position()

    return global_best_position

# 示例用法
n_iterations = 100
n_particles = 50
n_dimensions = 2
c1 = 2.0
c2 = 2.0

result = adaptive_pso(n_iterations, n_particles, n_dimensions, c1, c2)
print("Global best position:", result)

请注意，上述代码只是一个简单的示例，你需要根据你的具体问题定义适应度函数，并根据实际需求进行修改和调整。希望对你有所帮助！如果有任何问题，请随时提问。