python猴群算法

猴群算法是一种基于进化的优化算法，可以用来解决多种问题。其优点在于简单易懂，易于实现，适用于大规模问题的优化。

# 离散猴群算法实现
import random

# 定义适应度函数
def fitness_func(x):
    return sum(x)

# 初始化群体，随机生成N个个体，每个个体由D个元素组成
def init_population(N,D):
    population = []
    for i in range(N):
        individual = [random.randint(0,1) for _ in range(D)]
        population.append(individual)
    return population

# 评估群体适应度
def evaluate_population(population,fitness_func):
    scores = []
    for individual in population:
        score = fitness_func(individual)
        scores.append(score)
    return scores

# 筛选优秀个体，按照适应度从高到低排序，保留前P个
def select_population(population,scores,P):
    sorted_population = [x for _,x in sorted(zip(scores,population),key=lambda pair: pair[0],reverse=True)]
    return sorted_population[:P]

# 随机产生变异点，对个体进行变异
def mutate_individual(individual):
    mutant = individual[:]
    index = random.randint(0,len(individual)-1)
    mutant[index] = 1 - mutant[index]
    return mutant

# 交叉两个个体，返回两个新的个体
def crossover_individual(ind1,ind2):
    crossover_point = random.randint(1,len(ind1)-1)
    child1 = ind1[:crossover_point] + ind2[crossover_point:]
    child2 = ind2[:crossover_point] + ind1[crossover_point:]
    return child1,child2

# 根据已有种群生成新的种群
def breed_population(old_population,P,M,C,fitness_func):
    new_population = []
    scores = evaluate_population(old_population,fitness_func)
    elites = select_population(old_population,P)
    for i in range(len(old_population)-P):
        if random.random() 
以上是一个离散猴群算法的实现，其中关键的适应度函数、群体初始化、群体评估、繁殖操作等都有详细的实现。通过猴群算法，我们可以在较短时间内找到一个问题的近似最优解，非常实用。