CCPP路径规划遗传算法代码

以下是使用遗传算法解决CCPP（Combined Cycle Power Plant）路径规划问题的Python代码：

import random

# Constants
POP_SIZE = 50 # Population size
GENE_SIZE = 10 # Number of genes in each chromosome
MUTATION_RATE = 0.01 # Mutation rate
ELITE_RATE = 0.1 # Elite rate
GENERATIONS = 1000 # Number of generations

# Fitness function
def fitness(chromosome):
    # Calculate the total distance
    distance = 0
    for i in range(GENE_SIZE - 1):
        x1, y1 = chromosome[i]
        x2, y2 = chromosome[i+1]
        distance += ((x2-x1)**2 + (y2-y1)**2) ** 0.5
    
    return 1 / distance

# Selection function
def selection(population):
    # Sort the population by fitness
    population = sorted(population, key=lambda x: fitness(x), reverse=True)
    
    # Calculate the elite size
    elite_size = int(ELITE_RATE * POP_SIZE)
    
    # Select the elite chromosomes
    elite = population[:elite_size]
    
    # Select the remaining chromosomes using roulette wheel selection
    fitness_sum = sum([fitness(chromosome) for chromosome in population])
    roulette_wheel = [fitness(chromosome) / fitness_sum for chromosome in population]
    selection = random.choices(population, weights=roulette_wheel, k=POP_SIZE-elite_size)
    
    return elite + selection

# Crossover function
def crossover(parent1, parent2):
    # Select a random crossover point
    crossover_point = random.randint(1, GENE_SIZE-1)
    
    # Create the offspring chromosomes
    offspring1 = parent1[:crossover_point] + parent2[crossover_point:]
    offspring2 = parent2[:crossover_point] + parent1[crossover_point:]
    
    return offspring1, offspring2

# Mutation function
def mutation(chromosome):
    # Iterate over each gene
    for i in range(GENE_SIZE):
        # Apply the mutation rate
        if random.random() < MUTATION_RATE:
            # Swap the gene with another random gene
            j = random.randint(0, GENE_SIZE-1)
            chromosome[i], chromosome[j] = chromosome[j], chromosome[i]
    
    return chromosome

# Initialization
population = [[(random.uniform(0, 1), random.uniform(0, 1))) for j in range(GENE_SIZE)] for i in range(POP_SIZE)]

# Main loop
for generation in range(GENERATIONS):
    # Selection
    population = selection(population)
    
    # Crossover
    offspring = []
    for i in range(0, POP_SIZE-1, 2):
        offspring1, offspring2 = crossover(population[i], population[i+1])
        offspring.append(offspring1)
        offspring.append(offspring2)
    
    # Mutation
    population = [mutation(chromosome) for chromosome in offspring]
    
    # Print the best fitness
    best_fitness = fitness(population[0])
    print("Generation:", generation+1, "Best fitness:", best_fitness)

该代码使用遗传算法解决CCPP路径规划问题。在该问题中，我们需要找到一条连接多个点的路径，使得路径总长度最小。该代码的实现包括以下主要步骤：

1. 初始化种群：使用随机数生成器生成一组随机的染色体，每个染色体