MOAHA算法python代码

MOAHA算法是一种多目标优化算法，它基于粒子优化算法（PSO）和人工蜂群算法（ABC）的思想。下面是一个简单的MOAHA算法的Python代码示例：

import numpy as np

# 定义目标函数
def objective_function(x):
    return [x**2, (x-2)**2]

# 初始化参数
num_particles = 50
num_iterations = 100
num_objectives = 2
lower_bound = -5
upper_bound = 5

# 初始化粒子位置和速度
particles_position = np.random.uniform(lower_bound, upper_bound, (num_particles, num_objectives))
particles_velocity = np.zeros((num_particles, num_objectives))

# 初始化个体最优解和全局最优解
particles_best_position = particles_position.copy()
global_best_position = particles_best_position[np.argmin(objective_function(particles_best_position))]

# 开始迭代
for iteration in range(num_iterations):
    # 更新粒子速度和位置
    inertia_weight = 0.7  # 惯性权重
    cognitive_weight = 1.5  # 认知权重
    social_weight = 1.5  # 社会权重
    
    for i in range(num_particles):
        r1 = np.random.random()
        r2 = np.random.random()
        
        cognitive_component = cognitive_weight * r1 * (particles_best_position[i] - particles_position[i])
        social_component = social_weight * r2 * (global_best_position - particles_position[i])
        
        particles_velocity[i] = inertia_weight * particles_velocity[i] + cognitive_component + social_component
        particles_position[i] = particles_position[i] + particles_velocity[i]
        
        # 限制粒子位置在边界内
        particles_position[i] = np.clip(particles_position[i], lower_bound, upper_bound)
    
    # 更新个体最优解和全局最优解
    for i in range(num_particles):
        if objective_function(particles_position[i]) < objective_function(particles_best_position[i]):
            particles_best_position[i] = particles_position[i]
    
    global_best_position = particles_best_position[np.argmin(objective_function(particles_best_position))]

# 输出最优解
print("最优解：", global_best_position)

这段代码实现了一个简单的MOAHA算法，其中目标函数为一个二维函数，算法通过迭代更新粒子的速度和位置，寻找最优解。你可以根据自己的需求修改目标函数和算法参数。希望对你有帮助！