python写spea2的算法程序

以下是一个简单的Python SPEA2算法实现示例：

import random
import math

class SPEA2:
    def __init__(self, population_size, archive_size, max_generations, tournament_size, mutation_rate, crossover_rate, problem_size, bounds):
        self.population_size = population_size
        self.archive_size = archive_size
        self.max_generations = max_generations
        self.tournament_size = tournament_size
        self.mutation_rate = mutation_rate
        self.crossover_rate = crossover_rate
        self.problem_size = problem_size
        self.bounds = bounds
        self.population = []
        self.archive = []
        self.distances = {}
        self.fitness_values = {}
        
    def initialize_population(self):
        for i in range(self.population_size):
            individual = [random.uniform(self.bounds[j][0], self.bounds[j][1]) for j in range(self.problem_size)]
            self.population.append(individual)
            
    def evaluate_population(self):
        for individual in self.population:
            fitness = self.evaluate_fitness(individual)
            self.fitness_values[tuple(individual)] = fitness
            
    def evaluate_fitness(self, individual):
        # 这里是你的适应度函数，需要根据具体问题进行实现
        pass
    
    def environmental_selection(self):
        combined_population = self.population + self.archive
        self.distances = self.calculate_distances(combined_population)
        self.fitness_values.update(self.calculate_raw_fitness(combined_population))
        
        # 将个体按照适应度值排序
        sorted_individuals = sorted(list(self.fitness_values.items()), key=lambda x: x[1])
        
        # 选取前archive_size个个体作为存档
        self.archive = [list(individual) for individual, _ in sorted_individuals[:self.archive_size]]
        
        # 选取前population_size个个体作为下一代种群
        self.population = [list(individual) for individual, _ in sorted_individuals[:self.population_size]]
    
    def calculate_distances(self, population):
        distances = {}
        for i in range(len(population)):
            for j in range(i+1, len(population)):
                dist = math.sqrt(sum([(population[i][k] - population[j][k])**2 for k in range(self.problem_size)]))
                distances[(tuple(population[i]), tuple(population[j]))] = dist
        return distances
    
    def calculate_raw_fitness(self, population):
        fitness_values = {}
        for individual in population:
            fitness = 0
            for other_individual in population:
                if individual != other_individual:
                    fitness += self.calc_distance(individual, other_individual)
            fitness_values[tuple(individual)] = fitness
        return fitness_values
    
    def calc_distance(self, individual1, individual2):
        # 计算两个个体的距离
        if (tuple(individual1), tuple(individual2)) in self.distances:
            return self.distances[(tuple(individual1), tuple(individual2))]
        elif (tuple(individual2), tuple(individual1)) in self.distances:
            return self.distances[(tuple(individual2), tuple(individual1))]
        else:
            dist = math.sqrt(sum([(individual1[k] - individual2[k])**2 for k in range(self.problem_size)]))
            self.distances[(tuple(individual1), tuple(individual2))] = dist
            return dist
    
    def tournament_selection(self, population):
        tournament = random.sample(population, self.tournament_size)
        tournament.sort(key=lambda x: self.fitness_values[tuple(x)])
        return tournament[0]
    
    def crossover(self, parent1, parent2):
        if random.random() < self.crossover_rate:
            crossover_point = random.choice(range(self.problem_size))
            child1 = parent1[:crossover_point] + parent2[crossover_point:]
            child2 = parent2[:crossover_point] + parent1[crossover_point:]
            return child1, child2
        else:
            return parent1, parent2
    
    def mutation(self, individual):
        mutated = False
        for i in range(self.problem_size):
            if random.random() < self.mutation_rate:
                individual[i] += random.gauss(0, 1) * (self.bounds[i][1] - self.bounds[i][0]) / 10.0
                mutated = True
        if mutated:
            return individual
        else:
            return None
    
    def run(self):
        self.initialize_population()
        self.evaluate_population()
        for generation in range(self.max_generations):
            offspring = []
            for i in range(self.population_size):
                parent1 = self.tournament_selection(self.population)
                parent2 = self.tournament_selection(self.population)
                child1, child2 = self.crossover(parent1, parent2)
                mutated_child1 = self.mutation(child1)
                mutated_child2 = self.mutation(child2)
                if mutated_child1:
                    offspring.append(mutated_child1)
                if mutated_child2:
                    offspring.append(mutated_child2)
            self.population.extend(offspring)
            self.evaluate_population()
            self.environmental_selection()

在使用时，需要根据具体的问题进行适应度函数的实现，并设置算法的超参数。