组合作业车间调度问题的 粒子群优化_python_代码_下载

import random import math """The Job Shop Scheduling Problem using the Particle Swarm Optimization""" duration_of_job = [] deadline_of_job = [] def initial_input(): global population_size global number_of_jobs global working_matrix global velocity_matrix global personal_best_matrix global global_best_matrix """number_of_jobs = int(input("Enter the number of jobs:")) for x in range(0, number_of_jobs): print("For job", x + 1) duration_of_job.append(int(input("Enter the duration of job:"))) deadline_of_job.append(int(input("Enter the deadline of job:")))""" print("Now we begin with Particle Swarm Optimization") population_size = int(input("Enter the population size:")) working_matrix = [0 for x in range(population_size)] velocity_matrix = [[] for x in range(population_size)] personal_best_matrix = [0 for x in range(population_size)] global_best_matrix = [0 for x in range(population_size)] print("And now we have everything we need from you. ThankYou Very Much") def read_file(): global duration_of_job global deadline_of_job global number_of_jobs even_count = 0 with open("saving_integers.txt") as file: for line in file: working_list = (list(map(int, line.split()))) for x in working_list: if (even_count % 2 == 0): duration_of_job.append(working_list[even_count]) even_count += 1 else: deadline_of_job.append(working_list[even_count]) even_count += 1 number_of_jobs = len(duration_of_job) def initialization(): """Random initialization of positions, personal best matrix, global best solution and velocity matrix""" global working_matrix global personal_best_matrix global global_best_matrix global_best_matrix = random.sample(range(1, number_of_jobs + 1), number_of_jobs) for i in range(population_size): working_matrix[i] = random.sample(range(1, number_of_jobs + 1), number_of_jobs) personal_best_matrix[i] = random.sample(range(1, number_of_jobs + 1), number_of_jobs) if fitness_value(personal_best_matrix[i]) > fitness_value(global_best_matrix): global_best_matrix = personal_best_matrix[i] print("Initialization Complete") def fitness_value(given=[]): delay = [] total_time_taken = 0 delay_made = 0 for x in range(number_of_jobs): total_time_taken += duration_of_job[given[x] - 1] delay_made = total_time_taken - deadline_of_job[given[x] - 1] if (delay_made < 0): delay_made = 0 delay.append(delay_made) return 1 + (1 / sum(delay)) def fitness_value_normal(given=[]): delay = [] total_time_taken = 0 delay_made = 0 for x in range(number_of_jobs): total_time_taken += duration_of_job[given[x] - 1] delay_made = total_time_taken - deadline_of_job[given[x] - 1] if (delay_made < 0): delay_made = 0 delay.append(delay_made) return sum(delay) def create_a_swap_sequence(a=[], b=[]): global number_of_jobs return_matrix = [] for x in range(number_of_jobs): for y in range(number_of_jobs): if a[x] == b[y] and x != y: return_matrix = return_matrix + [x, y] return return_matrix def create_new_velocity_matrix(): global population_size global personal_best_matrix global global_best_matrix global working_matrix global velocity_matrix global number_of_jobs for x in range(population_size): velocity_matrix[x] = velocity_matrix[x] + create_a_swap_sequence(personal_best_matrix[x], working_matrix[x]) velocity_matrix[x] = velocity_matrix[x] + create_a_swap_sequence(global_best_matrix, working_matrix[x]) def swap(a=[], b=[]): x = 0 while x < len(b): temp = a[b[x]] a[b[x]] = a[b[x + 1]] a[b[x + 1]] = temp x += 2 return a def create_new_working_matrix(): global velocity_matrix global working_matrix global personal_best_matrix global global_best_matrix for x in range(population_size): working_matrix[x] = swap(working_matrix[x], velocity_matrix[x]) if fitness_value(working_matrix[x]) > fitness_value(personal_best_matrix[x]): personal_best_matrix[x] = working_matrix[x] if fitness_value(working_matrix[x]) > fitness_value(global_best_matrix): global_best_matrix = working_matrix[x] def print_solution(): global global_best_matrix print("\n\nThe best solution is: " + str(global_best_matrix) + " with a fitness value of: " + str(fitness_value( global_best_matrix)) + " and the normal fitness value of: " + str(fitness_value_normal(global_best_matrix))) def debug_print(): global population_size global number_of_jobs global working_matrix global velocity_matrix global personal_best_matrix global global_best_matrix print( "\n Population Size: " + str(population_size) + "\n Number of Jobs: " + str( number_of_jobs) + "\n Working Matrix: " + str(working_matrix) + "\n Velocity Matrix: " + str( velocity_matrix) + "\n Personal Best Matrix: " + str( personal_best_matrix) + "\n Global Best Matrix: " + str(global_best_matrix)) print( "\n Length of Velocity Matrix: " + str(len(velocity_matrix)) + "\n and the length of the first part is: " + str( len(velocity_matrix[0]))) def working(): iterations = 10 read_file() initial_input() initialization() for x in range(iterations): create_new_velocity_matrix() create_new_working_matrix() """debug_print()""" print_solution() working()