多目标粒子群算法python

多目标粒子群算法（Multi-objective Particle Swarm Optimization，MOPSO）是一种用于解决多目标优化问题的进化算法。它基于粒子群优化算法（Particle Swarm Optimization，PSO）并进行了扩展。

以下是一个使用Python实现的简单多目标粒子群算法的示例代码：

import numpy as np

class Particle:
    def __init__(self, bounds):
        self.position = np.random.uniform(bounds[0], bounds[1])
        self.velocity = np.zeros_like(self.position)
        self.best_position = self.position.copy()

class MOPSO:
    def __init__(self, obj_func, num_particles, num_iterations, bounds):
        self.obj_func = obj_func
        self.num_particles = num_particles
        self.num_iterations = num_iterations
        self.bounds = bounds

    def optimize(self):
        swarm = [Particle(self.bounds) for _ in range(self.num_particles)]
        global_best_position = None

        for _ in range(self.num_iterations):
            for particle in swarm:
                # 更新粒子速度和位置
                inertia_weight = 0.5
                cognitive_weight = 2.0
                social_weight = 2.0

                random1 = np.random.random(size=len(particle.position))
                random2 = np.random.random(size=len(particle.position))

                particle.velocity = (inertia_weight * particle.velocity +
                                     cognitive_weight * random1 * (particle.best_position - particle.position) +
                                     social_weight * random2 * (global_best_position - particle.position))
                particle.position += particle.velocity

                # 限制粒子位置在指定范围内
                particle.position = np.clip(particle.position, self.bounds[0], self.bounds[1])

                # 更新粒子的最佳位置
                if global_best_position is None or self.obj_func(particle.position) < self.obj_func(particle.best_position):
                    particle.best_position = particle.position.copy()

                # 更新全局最佳位置
                if global_best_position is None or self.obj_func(particle.position) < self.obj_func(global_best_position):
                    global_best_position = particle.position.copy()

        return global_best_position

# 示例使用的目标函数，可以根据实际问题修改
def objective_function(x):
    f1 = x[0]**2 + x[1]**2
    f2 = (x[0] - 1)**2 + (x[1] - 1)**2
    return f1, f2

# 设置算法参数并运行优化
num_particles = 50
num_iterations = 100
bounds = [np.array([-5, -5]), np.array([5, 5])]

mopso = MOPSO(objective_function, num_particles, num_iterations, bounds)
best_position = mopso.optimize()

print("Best position:", best_position)
print("Objective values:", objective_function(best_position))

以上代码只是一个简单的示例，实际应用中可能需要根据具体问题进行适当的调整和改进。希望对你有所帮助！如果还有其他问题，请随时提问。