def scalarizing_moop(instance, C=[1,10,100]):
sols = []
exec_time = []
for i in range(len(C)):
start_time = time.time()
instance.set_renting_ratio(C[i])
TTP_solver = TTPSolver(instance)
sol = TTP_solver.hyprid_EA(initial_strategy='greedy',
n_population=10,n_offspring= 10,
local_search=True,verbose=False)
print(i)
sols.append((i,sol,sol.profit,np.ceil(sol.duration).astype(int)))
exec_time.append(time.time()-start_time)
return np.array(sols), exec_time
label='Mean')
axes[1, 1].legend()
#plt.figure(figsize=(8,6))
for i, n_p in enumerate(n_pop):
axes[2, 1].errorbar(range(o),
mean_time[i, :],
std_time[i, :],
label='Pop size = ' + str(n_p))
axes[2, 1].set_xticks(range(o))
axes[2, 1].set_xticklabels(n_offspring)
axes[2, 1].errorbar(range(o),
np.mean(mean_time, axis=0),
np.mean(std_time, axis=0),
linestyle='--',
linewidth=2,
label='Mean')
axes[2, 1].legend()
KP_instance = KPSolver("instances/kp/kp-n279.txt")
TSP_instance = TSPSolver("instances/tsp/tsp-a280.txt")
TTP_inst = TTPInstance("instances/ttp/ttp-a280-n279.txt")
TTP_instance = TTPSolver(TTP_inst)
n_pop = [5, 10, 20, 50, 100]
n_offspring = [10, 20, 50, 100, 200]
#data = EA_paramters_tuning(TTP_instance,n_pop=n_pop,n_offspring=n_offspring,problem = 'TTP',initial_strategy = 'random',local_search=True,verbose = True, repeat= 10)
plot(data, n_pop, n_offspring)
from core.ttp import TTPSolver, TTPInstance
import numpy as np
import matplotlib.pyplot as plt
from glob import glob
import time
import pandas as pd
import seaborn as sns
"""
Test file fot TTP
"""
TTP_inst = TTPInstance("instances/ttp/ttp-a280-n279.txt")
TTP_solver = TTPSolver(TTP_inst)
def sequential_non_dominated(sol, sorted_archive):
if len(sorted_archive) == 0:
sorted_archive.append([sol])
else:
for i, sol_list in enumerate(sorted_archive):
larger_prof = False
inferior_idx = None
for idx, pl_sol in enumerate(sol_list):
if pl_sol[0] > sol[0]:
larger_prof = True
break
else:
if pl_sol[1] > sol[1] and inferior_idx is None:
inferior_idx = idx
if larger_prof:
sub_data.append((solution.score, time() - start))
"""
data.append(sub_data)
# If you add a strategy, please add its label here:
strategies_labels = ("independent", "local_search", "annealing") #, "hybrid_EA/20/20")
data = np.array(data).transpose(1, 0, 2)
return strategies_labels, data
for instance_uri in instances_uris:
try:
instance = TTPSolver(TTPInstance(instance_uri))
except:
continue
name = instance_uri.split("/")[-1]
# Get a array of size (n_strategies x n_repeat x 2)
# The dimension of size 3 is for (length, time duration)
# The dimension of size n_repeat is for average and standard deviation purpose
strategies_labels, data = try_strategies(instance)
plt.boxplot(data[:, :, 0].T)
plt.xticks(range(1, len(strategies_labels) + 1), strategies_labels, rotation=45)
plt.title("score for: " + name)
plt.savefig("./experiment_data/overall_analysis/ttp/" + name + ".score.png", bbox_inches='tight')
plt.clf()