def initial_solution(self):
"""
初始化贪心算法的路径，以便后续模拟退火算法来进行优化
:return: 贪心算法最短路径已经路径长度
"""
i_nodes = random.choice(self.nodes)
# 随机寻找初始城市/节点
node_solution = [i_nodes]
# 节点加入列表
f_nodes = set(self.nodes)
# 定义空闲城市列表/节点
f_nodes.remove(i_nodes)
# 释放随机寻找到的初始城市/节点
while f_nodes:
next_node = min(f_nodes, key=lambda x: self.dist(i_nodes, x))
# 求最近节点
f_nodes.remove(next_node)
node_solution.append(next_node)
# 添加最近节点至解决路径里
i_nodes = next_node
# 遍历节点
current_fit = self.fitness(node_solution)
if current_fit < self.best_fitness:
self.best_fitness = current_fit
self.best_solution = node_solution
self.fitness_list.append(current_fit)
visualize_tsp.plotTSP([node_solution], self.places)
return node_solution, current_fit
def visualize_routes(self):
"""
Visualize the TSP route with matplotlib.
"""
visualize_tsp.plotTSP([self.best_solution], self.coords)
from anneal import SimAnneal
from visualize_tsp import plotTSP
import matplotlib.pyplot as plt
import random, os, sys, time
def dotrial(t):
print("doing %d" % t.id)
t.anneal()
return t
if __name__ == '__main__':
ncities=100
ntrials=int(sys.argv[1])
random.seed(0)
coords = [[round(random.uniform(-1000,1000),4),round(random.uniform(-1000,1000),4)] for i in range(ncities)]
plotTSP([range(ncities),], coords)
t0=time.time()
trials = [SimAnneal(coords, stopping_iter = 500000, alpha=0.9995, id=i) for i in range(ntrials)]
got=map(dotrial, trials)
best=sorted([(t.best_fitness, t) for t in got])[0][1]
print("Time %f, Fitness %f" % (time.time()-t0, best.best_fitness))
best.visualize_routes()
best.plot_learning()
def visualizeRotes(self):
'''
Visualize the TSP route with matplotlib
'''
visualize_tsp.plotTSP([self.best_solution], self.coords)
def visualize_routes(self):
visualize_tsp.plotTSP([self.best_solution], self.coords)
from sa import sa
import visualize_tsp
if __name__ == "__main__":
sa1 = sa()
sa1.ann()
sol, pla = sa1.r_sol()
visualize_tsp.plotTSP([sol], pla)
