目标函数函数为平方和，取值范围bounds为-100到100，完善JADE代码,并输出best_solution

根据您提供的信息，我们可以将目标函数设置为平方和，并将取值范围 `bounds` 设置为 -100 到 100。以下是完善后的 JADE 代码，并输出 `best_solution`：

import numpy as np

def objective_func(x):
    return np.sum(np.square(x))

def JADE(objective_func, bounds, population_size=50, max_generations=1000, c=0.1, p=0.05, archive_size=50):
    # 初始化种群
    population = np.random.uniform(bounds[0], bounds[1], size=(population_size, len(bounds)))
    
    # 初始化个体适应度
    fitness = np.zeros(population_size)
    
    # 初始化个体适应度历史记录
    fitness_history = []
    
    # 初始化全局最优解及其适应度
    best_solution = None
    best_fitness = float('inf')
    
    # 初始化归档
    archive = []
    
    # 迭代搜索
    for generation in range(max_generations):
        for i in range(population_size):
            # 生成新个体
            mutant = population[np.random.choice(population_size, size=1)[0]]
            for j in range(len(bounds)):
                if np.random.rand() < p:
                    mutant[j] = mutant[j] + c * (np.random.rand() - 0.5) * (bounds[1][j] - bounds[0][j])
            
            # 选择当前个体与新个体中适应度较好的一个作为子代
            trial = mutant.copy()
            if objective_func(mutant) < fitness[i]:
                trial_fitness = objective_func(mutant)
                population[i] = trial
                fitness[i] = trial_fitness
            else:
                trial_fitness = fitness[i]
            
            # 更新全局最优解
            if trial_fitness < best_fitness:
                best_solution = trial.copy()
                best_fitness = trial_fitness
            
            # 更新归档
            if len(archive) < archive_size:
                archive.append(trial.copy())
            else:
                archive[np.random.choice(archive_size, size=1)[0]] = trial.copy()
        
        # 记录当前代的最优适应度
        fitness_history.append(best_fitness)
        
    return best_solution, best_fitness, fitness_history

bounds = [-100, 100]
best_solution, _, _ = JADE(objective_func, bounds)
print("Best Solution:", best_solution)

运行上述代码，即可输出 JADE 算法得到的最优解 `best_solution`。请注意，由于 JADE 是一种随机性优化算法，每次运行结果可能会有所不同。