无人机路径规划代码

以下是一个基于遗传算法的无人机路径规划的Python代码实现：

import random

class Gene:
    def __init__(self, x, y):
        self.x = x
        self.y = y
    
    def __str__(self):
        return f'({self.x}, {self.y})'

class Chromosome:
    def __init__(self, genes):
        self.genes = genes
        self.fitness = 0
    
    def __str__(self):
        return ' -> '.join(str(g) for g in self.genes)

class GA:
    def __init__(self, map, population_size, mutation_rate, elite_rate, crossover_rate, max_generation):
        self.map = map
        self.width = len(map[0])
        self.height = len(map)
        self.population_size = population_size
        self.mutation_rate = mutation_rate
        self.elite_rate = elite_rate
        self.crossover_rate = crossover_rate
        self.max_generation = max_generation
        self.population = []
    
    def generate_population(self):
        for i in range(self.population_size):
            genes = [Gene(random.randint(0, self.width-1), random.randint(0, self.height-1)) for j in range(self.width)]
            chromosome = Chromosome(genes)
            self.population.append(chromosome)
    
    def evaluate_fitness(self):
        for chromosome in self.population:
            path_length = 0
            for i in range(self.width-1):
                x1, y1 = chromosome.genes[i].x, chromosome.genes[i].y
                x2, y2 = chromosome.genes[i+1].x, chromosome.genes[i+1].y
                path_length += abs(x1 - x2) + abs(y1 - y2)
            chromosome.fitness = 1 / path_length
    
    def select_elite(self):
        elite_size = int(self.elite_rate * self.population_size)
        elite = sorted(self.population, key=lambda c: c.fitness, reverse=True)[:elite_size]
        return elite
    
    def selection(self):
        total_fitness = sum(c.fitness for c in self.population)
        p = [c.fitness / total_fitness for c in self.population]
        indices = random.choices(range(self.population_size), weights=p, k=self.population_size)
        return [self.population[i] for i in indices]
    
    def crossover(self, parent1, parent2):
        if random.random() > self.crossover_rate:
            return parent1, parent2
        
        start = random.randint(0, self.width-1)
        end = random.randint(start, self.width-1)
        genes1 = [g for g in parent1.genes if g not in parent2.genes[start:end+1]]
        genes2 = [g for g in parent2.genes if g not in parent1.genes[start:end+1]]
        genes = genes1[:start] + parent2.genes[start:end+1] + genes1[start:]
        child1 = Chromosome(genes)
        genes = genes2[:start] + parent1.genes[start:end+1] + genes2[start:]
        child2 = Chromosome(genes)
        return child1, child2
    
    def mutation(self, chromosome):
        if random.random() > self.mutation_rate:
            return chromosome
        
        indices = random.sample(range(self.width), 2)
        i, j = indices[0], indices[1]
        chromosome.genes[i], chromosome.genes[j] = chromosome.genes[j], chromosome.genes[i]
        return chromosome
    
    def evolve(self):
        self.generate_population()
        
        for generation in range(self.max_generation):
            self.evaluate_fitness()
            elite = self.select_elite()
            offspring = []
            
            while len(offspring) < self.population_size - len(elite):
                parents = random.sample(self.selection(), 2)
                children = self.crossover(parents[0], parents[1])
                offspring.extend(children)
            
            offspring = [self.mutation(c) for c in offspring]
            self.population = elite + offspring
            
            best_chromosome = max(self.population, key=lambda c: c.fitness)
            print(f'Generation {generation+1}: Best Path = {best_chromosome}, Fitness = {best_chromosome.fitness}')

这里的 `Gene` 类代表地图上的一个点，包含其坐标。 `Chromosome` 类代表一个无人机路径，由多个 `Gene` 组成。 `GA` 类中的 `generate_population` 方法用于初始化种群，`evaluate_fitness` 方法用于评估每个个体的适应度，`select_elite` 方法用于选择精英个体，`selection` 方法用于选择父代，`crossover` 方法用于交叉产生后代，`mutation` 方法用于变异产生新个体，`evolve` 方法实现了遗传算法的主要逻辑，包括选择、交叉、变异等步骤，通过不断迭代，得到最优的无人机路径。