MOPSO python代码

MOPSO（Multi-Objective Particle Swarm Optimization）是一种多目标粒子群优化算法，用于解决多目标优化问题。下面是一个简单的MOPSO的Python代码示例：

import numpy as np

class Particle:
    def __init__(self, num_objectives, num_variables):
        self.position = np.random.rand(num_variables)
        self.velocity = np.random.rand(num_variables)
        self.best_position = self.position.copy()
        self.objectives = np.zeros(num_objectives)
    
    def update_velocity(self, global_best_position, inertia_weight, cognitive_weight, social_weight):
        r1 = np.random.rand(len(self.velocity))
        r2 = np.random.rand(len(self.velocity))
        cognitive_component = cognitive_weight * r1 * (self.best_position - self.position)
        social_component = social_weight * r2 * (global_best_position - self.position)
        self.velocity = inertia_weight * self.velocity + cognitive_component + social_component
    
    def update_position(self):
        self.position += self.velocity
    
    def evaluate_objectives(self, objective_functions):
        self.objectives = objective_functions(self.position)
    
    def update_best_position(self):
        if np.all(self.objectives < self.best_objectives):
            self.best_position = self.position.copy()
            self.best_objectives = self.objectives.copy()

def mopso(objective_functions, num_particles, num_iterations):
    num_objectives = len(objective_functions)
    num_variables = len(objective_functions[0])
    
    particles = [Particle(num_objectives, num_variables) for _ in range(num_particles)]
    global_best_position = np.zeros(num_variables)
    global_best_objectives = np.inf * np.ones(num_objectives)
    
    for _ in range(num_iterations):
        for particle in particles:
            particle.evaluate_objectives(objective_functions)
            particle.update_best_position()
            if np.all(particle.objectives < global_best_objectives):
                global_best_position = particle.position.copy()
                global_best_objectives = particle.objectives.copy()
        
        for particle in particles:
            particle.update_velocity(global_best_position, inertia_weight=0.5, cognitive_weight=1.0, social_weight=1.0)
            particle.update_position()
    
    return global_best_position, global_best_objectives

使用该代码，你需要定义一个或多个目标函数，这些函数接受一个向量作为输入，并返回一个包含多个目标值的向量。然后，你可以调用`mopso`函数来运行MOPSO算法，并传入目标函数、粒子数量和迭代次数作为参数。该函数将返回找到的全局最优解的位置和目标值。