多目标粒子群算法python代码

根据提供的引用内容，以下是一个使用Python实现的多目标粒子群算法的示例代码：

import random

class Particle:
    def __init__(self, num_objectives, num_dimensions):
        self.position = [random.uniform(0, 1) for _ in range(num_dimensions)]
        self.velocity = [random.uniform(0, 1) for _ in range(num_dimensions)]
        self.best_position = self.position[:]
        self.objectives = [0] * num_objectives

    def update_velocity(self, global_best_position, inertia_weight, cognitive_weight, social_weight):
        for i in range(len(self.velocity)):
            cognitive_component = cognitive_weight * random.uniform(0, 1) * (self.best_position[i] - self.position[i])
            social_component = social_weight * random.uniform(0, 1) * (global_best_position[i] - self.position[i])
            self.velocity[i] = inertia_weight * self.velocity[i] + cognitive_component + social_component

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

    def evaluate_objectives(self, objective_functions):
        self.objectives = [objective_function(self.position) for objective_function in objective_functions]

class MOPSO:
    def __init__(self, num_particles, num_objectives, num_dimensions, num_iterations, inertia_weight, cognitive_weight, social_weight):
        self.num_particles = num_particles
        self.num_objectives = num_objectives
        self.num_dimensions = num_dimensions
        self.num_iterations = num_iterations
        self.inertia_weight = inertia_weight
        self.cognitive_weight = cognitive_weight
        self.social_weight = social_weight
        self.particles = []
        self.global_best_position = [random.uniform(0, 1) for _ in range(num_dimensions)]
        self.objective_functions = []

    def add_objective_function(self, objective_function):
        self.objective_functions.append(objective_function)

    def initialize_particles(self):
        for _ in range(self.num_particles):
            particle = Particle(self.num_objectives, self.num_dimensions)
            self.particles.append(particle)

    def update_global_best_position(self):
        for particle in self.particles:
            if all(particle.objectives[i] < self.particles[0].objectives[i] for i in range(self.num_objectives)):
                self.global_best_position = particle.position[:]

    def run(self):
        self.initialize_particles()
        for _ in range(self.num_iterations):
            for particle in self.particles:
                particle.evaluate_objectives(self.objective_functions)
                particle.update_velocity(self.global_best_position, self.inertia_weight, self.cognitive_weight, self.social_weight)
                particle.update_position()
                if all(particle.objectives[i] < particle.best_position[i] for i in range(self.num_objectives)):
                    particle.best_position = particle.position[:]
            self.update_global_best_position()

# 示例使用的目标函数
def objective_function1(position):
    return position[0] ** 2

def objective_function2(position):
    return (position[0] - 2) ** 2

mopso = MOPSO(num_particles=30, num_objectives=2, num_dimensions=1, num_iterations=100, inertia_weight=0.5, cognitive_weight=1, social_weight=1)
mopso.add_objective_function(objective_function1)
mopso.add_objective_function(objective_function2)
mopso.run()