补充实现遗传算法和粒子群算法的具体逻辑

好的，下面我将补充遗传算法和粒子群优化算法的具体逻辑。请注意，以下代码仅为示例，您需要根据具体问题进行适当的修改和调整。

import numpy as np

# 遗传算法
def genetic_algorithm(population_size=50, num_generations=100, crossover_rate=0.8, mutation_rate=0.1):
    # 初始化种群
    population = np.random.rand(population_size, 2) * 100
    best_solution = None
    best_fitness = float('inf')

    for generation in range(num_generations):
        # 计算适应度
        fitness = evaluate_fitness(population)

        # 选择操作
        selected_indices = selection(fitness)

        # 交叉操作
        offspring = crossover(population[selected_indices], crossover_rate)

        # 变异操作
        offspring = mutation(offspring, mutation_rate)

        # 合并种群
        population = np.vstack((population, offspring))

        # 更新最优解
        best_index = np.argmin(fitness)
        if fitness[best_index] < best_fitness:
            best_solution = population[best_index]
            best_fitness = fitness[best_index]

        # 选择下一代种群
        population = selection(population, fitness, population_size)

    return best_solution


# 粒子群优化算法
def particle_swarm_optimization(num_particles=50, max_iterations=100, inertia_weight=0.7, cognitive_weight=1.4, social_weight=1.4):
    # 初始化粒子位置和速度
    positions = np.random.rand(num_particles, 2) * 100
    velocities = np.random.rand(num_particles, 2)

    # 初始化全局最优解
    global_best_solution = None
    global_best_fitness = float('inf')

    for iteration in range(max_iterations):
        # 计算适应度
        fitness = evaluate_fitness(positions)

        # 更新全局最优解
        best_index = np.argmin(fitness)
        if fitness[best_index] < global_best_fitness:
            global_best_solution = positions[best_index]
            global_best_fitness = fitness[best_index]

        # 更新粒子速度和位置
        velocities = update_velocities(velocities, positions, global_best_solution, inertia_weight, cognitive_weight, social_weight)
        positions = update_positions(positions, velocities)

    return global_best_solution


# 遗传算法的选择操作
def selection(population, fitness, population_size):
    # 根据适应度进行选择操作（例如轮盘赌选择、锦标赛选择等）
    selected_indices = np.random.choice(len(population), size=population_size, replace=True, p=fitness/np.sum(fitness))
    return selected_indices


# 遗传算法的交叉操作
def crossover(parents, crossover_rate):
    # 根据交叉概率进行交叉操作（例如单点交叉、多点交叉等）
    offspring = np.zeros_like(parents)
    for i in range(0, len(parents), 2):
        if np.random.rand() < crossover_rate:
            crossover_point = np.random.randint(1, parents.shape[1])
            offspring[i] = np.concatenate((parents[i][:crossover_point], parents[i+1][crossover_point:]))
            offspring[i+1] = np.concatenate((parents[i+1][:crossover_point], parents[i][crossover_point:]))
        else:
            offspring[i] = parents[i]
            offspring[i+1] = parents[i+1]
    return offspring


# 遗传算法的变异操作
def mutation(offspring, mutation_rate):
    # 根据变异概率进行变异操作（例如位变异、高斯变异等）
    for i in range(len(offspring)):
        if np.random.rand() < mutation_rate:
            gene_index = np.random.randint(len(offspring[i]))
            offspring[i][gene_index] += np.random.randn() * 0.1
            offspring[i] = np.clip(offspring[i], 0, 100)
    return offspring


# 粒子群优化算法的速度更新
def update_velocities(velocities, positions, global_best_solution, inertia_weight, cognitive_weight, social_weight):
    # 根据速度更新公式更新粒子速度
    new_velocities = inertia_weight * velocities + cognitive_weight * np.random.rand() * (positions - positions) + social_weight * np.random.rand() * (global_best_solution - positions)
    return new_velocities


# 粒子群优化算法的位置更新
def update_positions(positions, velocities):
    # 根据位置更新公式更新粒子位置
    new_positions = positions + velocities
    new_positions = np.clip(new_positions, 0, 100)
    return new_positions

在上述代码中，我补充了遗传算法和粒子群优化算法的具体逻辑。您可以根据需要对其进行修改和调整，以适应您的具体问题。

希望这次的补充满足了您的需求。如果还有其他问题，请随时提问！