多参量离散变量全局优化算法代码

以下是一个多参量离散变量全局优化算法的示例代码：

import numpy as np

def objective_function(x):
    # 定义目标函数，这里以一个简单的二维函数为例
    return np.sin(x[0]) + np.cos(x[1])

def generate_random_solution(bounds):
    # 在搜索空间中生成一个随机解
    solution = []
    for lower_bound, upper_bound in bounds:
        solution.append(np.random.randint(lower_bound, upper_bound+1))
    return solution

def discrete_global_optimization(bounds, max_iter):
    # 初始化搜索空间和最优解
    best_solution = None
    best_fitness = float('-inf')
    
    # 开始迭代搜索
    for _ in range(max_iter):
        # 生成一个随机解
        solution = generate_random_solution(bounds)
        
        # 计算解的适应度值
        fitness = objective_function(solution)
        
        # 如果当前解更优，则更新最优解
        if fitness > best_fitness:
            best_solution = solution
            best_fitness = fitness
            
    return best_solution, best_fitness

# 定义搜索空间的边界
bounds = [(-10, 10), (-10, 10)]

# 运行离散变量全局优化算法
best_solution, best_fitness = discrete_global_optimization(bounds, max_iter=100)

# 输出最优解和最优适应度值
print("Best Solution:", best_solution)
print("Best Fitness:", best_fitness)

这段代码中，objective_function函数定义了一个简单的二维函数作为目标函数。generate_random_solution函数用于在搜索空间中生成一个随机解。discrete_global_optimization函数则实现了离散变量全局优化算法，其中max_iter参数指定了迭代次数。最后，将搜索空间的边界和迭代次数作为参数传入discrete_global_optimization函数即可运行算法