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GA_TSP.py
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GA_TSP.py
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import math
import timeit
import numpy as np
import random
from threading import Thread
import functools
# 시간제한 데코레이터
def timeout(seconds_before_timeout):
def deco(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
res = [Exception('function [%s] timeout [%s seconds] exceeded!' %(func.__name__, seconds_before_timeout))]
def newFunc():
try:
res[0] = func(*args, **kwargs)
except Exception as e:
res[0] = e
t = Thread(target=newFunc)
t.daemon = True
try:
t.start()
t.join(seconds_before_timeout)
except Exception as e:
print('error starting thread')
raise e
ret = res[0]
if isinstance(ret, BaseException):
raise ret
return ret
return wrapper
return deco
dist_ar = [] # 거리표(global)
limit_time = 36 # 제한시간(global)
cities_count = 0 # 도시 수(global)
dots_list = [] # 도시 리스트(global)
# Hyper Parameter
MUT = 0.2 # 변이확률
SEL = 0.85 # 선택압
END = 1000 # 최종세대 설정
chrCOUNT = 50 # 해집단 내 염색체 개수
selCOUNT = 25 # selection시 선택되는 상위 염색체의 개수
# 거리표 제작(param : 문제 경로) : dist_df
def make_distDataframe(str):
global dist_ar
global limit_time
global cities_count
global dots_list
reader = open(str, mode='rt', encoding='utf-8')
dots_list = reader.read().split("\n") # ['x1 y1', 'x2 y2', 'x3 y3' ... 'xn yn']
cities_count = int(dots_list.pop(0))
limit_time = float(dots_list.pop())
x_list = [] # ['x1', 'x2', 'x3' ... 'xn']
y_list = [] # ['y1', 'y2', 'y3' ... 'yn']
for i in range(cities_count):
temp = dots_list[i].split(" ")
x_list.append(float(temp[0]))
y_list.append(float(temp[1]))
dist_ar = []
for n in range(cities_count):
temp = []
for m in range(cities_count):
temp.append(round((math.sqrt(((x_list[m] - x_list[n]) ** 2) + ((y_list[m] - y_list[n]) ** 2))), 2))
dist_ar.append(temp)
dist_ar = np.array(dist_ar)
print(dist_ar)
# 거리표를 이용한 적합도 매칭 함수
def cal_fit(stri) :
fit = 0
for i in range(len(stri)-1) :
if i == len(stri)-1 :
fit += dist_ar[stri[i], stri[0]]
else :
fit += dist_ar[stri[i], stri[i+1]]
return fit
# 0 ~ ranges-1의 범위 중 두 개를 랜덤으로 샘플링해서 list 리턴
def randomTwo(ranges) :
randomList = []
randomList += random.sample(range(0,ranges), 2)
randomList.sort()
return randomList
def TSP_GA() :
# 환경 설정 및 초기화
generation = 1 # 현재 세대
population = [] # 현재 세대 or initializing시 최종 population
population_fit = [] # population의 적합도
populations = [] #population과 적합도로 이루어진 이차원 배열
step_result = [] # step을 거칠 때 변화된 population
# initialize
for i in range(chrCOUNT) :
population.append(random.sample(range(0, cities_count), cities_count))
for i in range(chrCOUNT) :
population_fit.append(round(cal_fit(population[i]), 5))
populations = np.array([population, population_fit])
populations = populations.T
# print('초기 염색체 : \n', population, '\n염색체 별 적합도 :\n', population_fit)
# print(populations)
while 1:
generation += 1
populations = populations[np.argsort(populations[:, 1])]
# selection : 토너먼트선택,
populations = populations[np.argsort(populations[:, 1])]
for endSel in range(selCOUNT):
# 난수룰 발생시켜 해집단 내 두 유전자 선택, 선택난수 발생
# 선택난수가 선택압보다 작으면 두 유전자 중 좋은 유전자가 선택. 아니면 반대로
parents_index = [0] * 2
for i in range(len(parents_index)):
selGeneNum = randomTwo((chrCOUNT - endSel))
match = random.random()
if match < SEL:
if populations[selGeneNum[0], 1] < populations[selGeneNum[1], 1]:
parents_index[i] = selGeneNum[0]
else:
parents_index[i] = selGeneNum[1]
else:
if populations[selGeneNum[0], 1] < populations[selGeneNum[1], 1]:
parents_index[i] = selGeneNum[1]
else:
parents_index[i] = selGeneNum[0]
# crossover : order-based crossover
daddy_value = populations[parents_index[0], 0].copy()
mommy_value = populations[parents_index[1], 0].copy()
CsGeneNum = randomTwo(cities_count)
offspring = daddy_value[CsGeneNum[0]: CsGeneNum[1]]
for i in daddy_value[CsGeneNum[0]: CsGeneNum[1]]:
mommy_value.remove(i)
for i in range(len(offspring)):
mommy_value.insert(CsGeneNum[0] + i, offspring[i])
offspring = mommy_value
offspring_fit = cal_fit(offspring)
# mutation : exchange mutation
mut_p = random.random()
if mut_p < MUT:
MtGeneNum = randomTwo(cities_count)
mut_Temp = offspring[MtGeneNum[0]]
offspring[MtGeneNum[0]] = offspring[MtGeneNum[1]]
offspring[MtGeneNum[1]] = mut_Temp
offspring_fit = cal_fit(offspring)
populations = np.vstack((populations, [offspring, offspring_fit]))
# Replacement
populations = populations[np.argsort(populations[:, 1])]
for i in range(chrCOUNT - selCOUNT):
np.delete(populations, (chrCOUNT + i), axis=0)
print(generation, '세대 최적 해 : \n', populations[0, 0], "\n", populations[0, 1])
@timeout(limit_time)
def start_GA(stri) :
make_distDataframe(stri)
TSP_GA()
try :
start = timeit.default_timer()
start_GA("2opt_dots/2opt_cycle100.in")
stop = timeit.default_timer()
print(stop - start)
except :
stop = timeit.default_timer()
print(stop - start)