-
Notifications
You must be signed in to change notification settings - Fork 0
/
CARP_solver.py
729 lines (628 loc) · 25.3 KB
/
CARP_solver.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
from collections import defaultdict
import numpy as np
import os
import multiprocessing
import re
import copy
import random
import sys
import time
# status
INFTY = 0x3f3f3f3f
NINFTY = -0x3f3f3f3f
ALPHA = 0.2 # 0.2
POPULATION_SIZE = 30 # 30
init_population = 500 # 500
CPU = 1
NAME = ""
VERTICES = None
DEPOT = None
REQUIRED_EDGES = None
NON_REQUIRED_EDGES = None
VEHICLES = None
CAPACITY = None
TOTAL_COST_OF_REQUIRED_EDGES = None
RATIO = None
EDGES = None
REDGE = None
GRAPH = set()
EC = None
ED = None
SHORTEST_DIS = None
TIME = 60 # 60
class Solution:
def __init__(self, routes, loads, costs, total_cost, capacity):
self.routes = routes
self.loads = loads
# self.costs = costs
self.total_cost = int(total_cost) if total_cost != np.inf else np.inf
self.capacity = capacity
self.load_exceed = sum([c - capacity for c in loads if c > capacity])
self.is_valid = self.load_exceed == 0
if self.is_valid:
self.nvg = 0
else:
self.nvg = 1
if self.loads:
self.discard_prop = 2 * self.load_exceed / sum(self.loads) * pow(3, self.nvg)
def init_valid(self):
self.load_exceed = sum([c - self.capacity for c in self.loads if c > self.capacity])
self.is_valid = self.load_exceed == 0
if not self.is_valid:
self.nvg += 1
self.discard_prop = 2 * self.load_exceed / sum(self.loads) * pow(3, self.nvg)
if self.routes.count([]):
for i, c in enumerate(self.routes):
if not c:
del self.routes[i]
del self.loads[i]
# del self.costs[i]
def __hash__(self):
return hash(str(self.routes))
def __eq__(self, other):
return self.routes == other.routes
# -------------------------------------------------------
# Read file
# -------------------------------------------------------
def set_opt(file_name):
global VERTICES, CAPACITY, DEPOT, REQUIRED_EDGES, NON_REQUIRED_EDGES, CPU, VEHICLES, TOTAL_COST_OF_REQUIRED_EDGES
CPU = multiprocessing.cpu_count()
if CPU not in range(1, 20):
CPU = 1
if CPU > 8:
CPU = 8
with open(file_name) as f:
array = f.readlines()
VERTICES = int(re.findall(": (.+?)\n", array[1])[0])
DEPOT = int(re.findall(": (.+?)\n", array[2])[0])
REQUIRED_EDGES = int(re.findall(": (.+?)\n", array[3])[0])
NON_REQUIRED_EDGES = int(re.findall(": (.+?)\n", array[4])[0])
VEHICLES = int(re.findall(": (.+?)\n", array[5])[0])
CAPACITY = int(re.findall(": (.+?)\n", array[6])[0])
TOTAL_COST_OF_REQUIRED_EDGES = int(re.findall(": (.+?)\n", array[7])[0])
global POPULATION_SIZE
# if VERTICES < 50:
# POPULATION_SIZE = 50
# else:
# POPULATION_SIZE=30
global EDGES, EC, ED, RATIO, SHORTEST_DIS, SHORTEST_PATH, REDGE
SHORTEST_DIS = np.full((VERTICES + 1, VERTICES + 1), fill_value=INFTY, dtype=int)
# SHORTEST_PATH = np.full((VERTICES + 1, VERTICES + 1), fill_value=None, dtype=list)
# np.fill_diagonal(SHORTEST_PATH, [])
EDGES = defaultdict(list)
EC = {}
ED = {}
REDGE = []
for line in array[9:-1]:
line = line.strip().split()
head = int(line[0])
tail = int(line[1])
GRAPH.add(head)
GRAPH.add(tail)
cost = int(line[2])
demand = int(line[3])
EDGES[head].append(tail)
EDGES[tail].append(head)
EC[(tail, head)] = cost
EC[(head, tail)] = cost
SHORTEST_DIS[head][tail] = cost
SHORTEST_DIS[tail][head] = cost
# SHORTEST_PATH[head][tail] = []
# SHORTEST_PATH[tail][head] = []
# SHORTEST_PATH[head][tail].append((head, tail))
# SHORTEST_PATH[tail][head].append((tail, head))
ED[(head, tail)] = demand
ED[(tail, head)] = demand
if demand:
REDGE.append((head, tail))
REDGE.append((tail, head))
def init_path():
global SHORTEST_DIS
gra = list(GRAPH)
for k in gra:
for i in gra:
for j in gra:
tmp = SHORTEST_DIS[i][k] + SHORTEST_DIS[k][j]
if SHORTEST_DIS[i][j] > tmp:
SHORTEST_DIS[i][j] = tmp
# SHORTEST_PATH[i][j] = SHORTEST_PATH[i][k] + SHORTEST_PATH[k][j]
# -------------------------------------------------------
np.fill_diagonal(SHORTEST_DIS, 0)
# Population initialization
# -------------------------------------------------------
def population_init(REDGE, ED, EC, SHORTEST_DIS, num, CAPACITY, DEPOT, seed, end, POPULATION_SIZE, VEHICLE):
population = set()
# random.seed(seed)
while len(population) < num:
sol = RPSH(REDGE, ED, EC, SHORTEST_DIS, CAPACITY, DEPOT)
if random.random() > sol.discard_prop:
population.add(sol)
best = searching(population, REDGE, ED, EC, SHORTEST_DIS, CAPACITY, DEPOT, seed, POPULATION_SIZE, end, VEHICLE)
return best
# -------------------------------------------------------
# Random Path Scanning Heuristic
# -------------------------------------------------------
def RPSH(REDGE, ED, EC, SHORTEST_DIS, CAPACITY, DEPOT):
paths = []
rearc = REDGE.copy()
random.shuffle(rearc) # Required arc, free
vehicles = -1 # vehicle number
loads = [] # loads of every vehicle
costs = [] # costs of every path
while True:
src = DEPOT
vehicles += 1
load = 0
cost = 0
path = []
while True:
cost_add = INFTY
edge_add = False
for edge in rearc:
if load + ED[edge] <= CAPACITY:
d_se = SHORTEST_DIS[src][edge[0]]
if d_se < cost_add:
cost_add = d_se
edge_add = edge
elif d_se == cost_add and better(edge, edge_add, DEPOT, load, ED, EC, SHORTEST_DIS,
CAPACITY):
edge_add = edge
if edge_add:
# path.extend(SHORTEST_PATH[(src,edge_add[0])])
path.append(edge_add)
rearc.remove(edge_add)
rearc.remove(inverseArc(edge_add))
load += ED[edge_add]
cost += (EC[edge_add] + cost_add)
src = edge_add[1]
if len(rearc) == 0 or cost_add == INFTY:
break
cost += SHORTEST_DIS[src][DEPOT]
costs.append(cost)
loads.append(load)
# path.extend(SHORTEST_PATH[(src, DEPOT)])
paths.append(path)
if len(rearc) == 0:
break
solution = Solution(paths, loads, costs, sum(costs), CAPACITY)
return solution
def better(tmp_edge, edge, src, load, ED, EC, SHORTEST_DIS, CAPACITY, rule=None):
if rule is None:
rule = random.randint(1, 12)
if not edge:
return True
else:
if rule < 3:
return check_ratio(tmp_edge, edge, ED, EC,
isMax=True)
elif rule < 5:
return check_ratio(tmp_edge, edge, ED, EC, isMax=False)
else:
tmp_edge_cost = SHORTEST_DIS[tmp_edge[1]][src]
edge_cost = SHORTEST_DIS[edge[1]][src]
if rule < 7: # maximize return cost
return tmp_edge_cost > edge_cost
elif rule < 9: # minimize return cost
return tmp_edge_cost < edge_cost
elif rule < 11:
if load > CAPACITY / 2:
return tmp_edge_cost > edge_cost
else: # else apply rule 4
return tmp_edge_cost < edge_cost
else:
return random.randint(0, 1)
# -------------------------------------------------------
# get inverse arc
# -------------------------------------------------------
def inverseArc(arc):
"""
:paraam arc: edge
"""
return arc[::-1]
def check_ratio(tmp_edge, edge, ED, EC, isMax=False):
tmp_edge_ratio = EC[tmp_edge] / ED[tmp_edge]
edge_ratio = EC[edge] / ED[edge]
if isMax:
return tmp_edge_ratio > edge_ratio # if tmp_edge has larger ratio, then return True else False
else:
return tmp_edge_ratio < edge_ratio # if tmp_edge has smaller ratio, then return True else False
# -------------------------------------------------------
# Calculate the demand for a given path
# -------------------------------------------------------
def cal_demand(path):
demand = 0
for edge in path:
demand += ED[edge]
return demand
# -------------------------------------------------------
# Calculate the cost for a given path
# -------------------------------------------------------
def cal_cost(paths, REDGE, ED, EC, SHORTEST_DIS, CAPACITY, DEPOT):
costs = 0
for path in paths:
for edge in path:
costs += EC[edge]
for edge_index in range(len(path) - 1):
costs += SHORTEST_DIS[path[edge_index][1]][path[edge_index + 1][0]]
costs += SHORTEST_DIS[DEPOT][path[0][0]] + SHORTEST_DIS[path[-1][1]][DEPOT]
return costs
# def cal_cost1(paths, REDGE, ED, EC, SHORTEST_DIS, CAPACITY, DEPOT):
# costs = 0
# a = []
# for path in paths:
# u = 0
# for edge in path:
# costs += EC[edge]
# u += EC[edge]
# for edge_index in range(len(path) - 1):
# costs += SHORTEST_DIS[path[edge_index][1]][path[edge_index + 1][0]]
# u += SHORTEST_DIS[path[edge_index][1]][path[edge_index + 1][0]]
# costs += SHORTEST_DIS[DEPOT][path[0][0]] + SHORTEST_DIS[path[-1][1]][DEPOT]
# u += SHORTEST_DIS[DEPOT][path[0][0]] + SHORTEST_DIS[path[-1][1]][DEPOT]
# a.append(u)
# return costs, a
def single_insertion(solution, REDGE, ED, EC, SHORTEST_DIS, CAPACITY, DEPOT, VEHICLE):
# get selected task index
new_solution = copy.deepcopy(solution)
routes: list = new_solution.routes
sai = random.randrange(0, len(routes)) # start <= N < end
sa = routes[sai]
sti = random.randrange(0, len(sa)) # start <= N < end
# information used in calculation
u, v = sa[sti]
task = (u, v)
# calculate changed selected arc costs
pre_end = sa[sti - 1][1] if sti != 0 else DEPOT
next_start = sa[sti + 1][0] if sti != len(
sa) - 1 else DEPOT
cc = SHORTEST_DIS[pre_end][next_start] - SHORTEST_DIS[pre_end][u] - SHORTEST_DIS[
v][next_start] - EC[task]
# new_solution.costs[sai] += cc
new_solution.total_cost += cc
new_solution.loads[sai] -= ED[task]
selected_task = sa.pop(sti)
# get inserted index
routes.append([])
iai = random.randrange(0, len(routes))
ia = routes[iai]
ip = random.randint(0, len(ia)) # start <= N <= end
# calculate changed inserted arc costs
pre_end = ia[ip - 1][1] if ip != 0 else DEPOT
next_start = ia[ip][0] if ip != len(ia) else DEPOT
cc = SHORTEST_DIS[pre_end][u] + SHORTEST_DIS[v][next_start] + EC[task] - SHORTEST_DIS[
pre_end][next_start]
rcc = SHORTEST_DIS[pre_end][v] + SHORTEST_DIS[u][next_start] + EC[task] - SHORTEST_DIS[
pre_end][next_start] # (v, u)
if rcc < cc:
selected_task = (v, u)
cc = rcc
if not ia: # means a new arc
# new_solution.costs.append(cc)
new_solution.loads.append(ED[task])
else:
del routes[-1]
# new_solution.costs[iai] += cc
new_solution.loads[iai] += ED[task]
new_solution.total_cost += cc
ia.insert(ip, selected_task)
new_solution.init_valid()
# if sum(solution.costs)== solution.total_cost:
# print(solution.is_valid)
# if cal_cost(solution.routes, REDGE, ED, EC, SHORTEST_DIS, CAPACITY, DEPOT)!=sum(solution.costs):
# print("sb")
# if cal_cost(new_solution.routes, REDGE, ED, EC, SHORTEST_DIS, CAPACITY, DEPOT)!=sum(new_solution.costs):
# print("sb11111111111111111111111111")
return new_solution
def double_insertion(solution, REDGE, ED, EC, SHORTEST_DIS, CAPACITY, DEPOT, VEHICLE):
# get selected first task index
new_solution = copy.deepcopy(solution)
routes: list = new_solution.routes
sai = random.randrange(0, len(routes)) # start <= N < end
while len(routes[sai]) < 2: # routes that size >= 2 can be applied DI
sai = random.randrange(0, len(routes))
sa = routes[sai]
sti = random.randrange(0, len(
sa) - 1) # start <= N < end - 1, should leave a position for second
# information used in calculation
u1, v1 = sa[sti]
u2, v2 = sa[sti + 1]
task1 = (u1, v1)
task2 = (u2, v2)
# calculate changed selected arc costs
pre_end = sa[sti - 1][1] if sti != 0 else DEPOT
next_start = sa[sti + 2][0] if sti != len(
sa) - 2 else DEPOT
cc = SHORTEST_DIS[pre_end][next_start] \
- SHORTEST_DIS[pre_end][u1] - EC[task1] - SHORTEST_DIS[v1][u2] - EC[task2] - SHORTEST_DIS[
v2][next_start]
# new_solution.costs[sai] += cc
new_solution.total_cost += cc
new_solution.loads[sai] -= ED[task1] + ED[task2]
t1 = sa.pop(sti)
t2 = sa.pop(sti)
routes.append([])
iai = random.randrange(0, len(routes))
ia = routes[iai]
ip = random.randint(0, len(ia)) # start <= N <= end
pre_end = ia[ip - 1][1] if ip != 0 else DEPOT
next_start = ia[ip][0] if ip != len(ia) else DEPOT
cc = SHORTEST_DIS[pre_end][u1] + EC[task1] + SHORTEST_DIS[v1][u2] + EC[task2] + SHORTEST_DIS[
v2][next_start] \
- SHORTEST_DIS[pre_end][next_start]
rcc = SHORTEST_DIS[pre_end][v2] + EC[task2] + SHORTEST_DIS[u2][v1] + EC[task1] + \
SHORTEST_DIS[u1][next_start] \
- SHORTEST_DIS[pre_end][next_start]
if rcc < cc:
t1 = (v2, u2)
t2 = (v1, u1)
cc = rcc
if not ia: # means a new arc
# new_solution.costs.append(cc)
new_solution.loads.append(ED[task1] + ED[task2])
else:
del routes[-1]
# new_solution.costs[iai] += cc
new_solution.loads[iai] += ED[task2] + ED[task1]
new_solution.total_cost += cc
ia.insert(ip, t2)
ia.insert(ip, t1)
new_solution.init_valid()
# if sum(solution.costs)== solution.total_cost:
# print(solution.is_valid)
# if cal_cost(new_solution.routes, REDGE, ED, EC, SHORTEST_DIS, CAPACITY, DEPOT)!=sum(new_solution.costs):
# print("sb11111111111111111111111111")
return new_solution
def swap(solution, REDGE, ED, EC, SHORTEST_DIS, CAPACITY, DEPOT, VEHICLE):
new_solution = copy.deepcopy(solution)
routes: list = new_solution.routes
# a, _ = cal_cost1(new_solution.routes, REDGE, ED, EC, SHORTEST_DIS, CAPACITY, DEPOT)
# get first selected task index
sai1 = random.randrange(0, len(routes)) # start <= N < end
sa1 = routes[sai1]
sti1 = random.randrange(0, len(sa1)) # start <= N < end
# get second selected task index
sai2 = random.randrange(0, len(routes)) # start <= N < end
sa2 = routes[sai2]
sti2 = random.randrange(0, len(sa2)) # start <= N < end
while sai1 == sai2 and sti1 == sti2:
sai2 = random.randrange(0, len(routes)) # start <= N < end
sa2 = routes[sai2]
sti2 = random.randrange(0, len(sa2)) # start <= N < end
# information used in calculation
u1, v1 = sa1[sti1]
u2, v2 = sa2[sti2]
task1 = (u1, v1)
task2 = (u2, v2)
pre_end1 = sa1[sti1 - 1][1] if sti1 != 0 else DEPOT
next_start1 = sa1[sti1 + 1][0] if sti1 != len(
sa1) - 1 else DEPOT
pre_end2 = sa2[sti2 - 1][1] if sti2 != 0 else DEPOT
next_start2 = sa2[sti2 + 1][0] if sti2 != len(
sa2) - 1 else DEPOT
selected_task1 = sa1.pop(sti1)
if sai1 == sai2 and sti1 < sti2:
selected_task2 = sa2.pop(sti2 - 1)
else:
selected_task2 = sa2.pop(sti2)
# first arc cost change : insert task2 into arc1
rc1 = SHORTEST_DIS[pre_end1][u1] + EC[task1] + SHORTEST_DIS[v1][next_start1]
cc1 = SHORTEST_DIS[pre_end1][u2] + EC[task2] + SHORTEST_DIS[v2][next_start1] - rc1
rcc1 = SHORTEST_DIS[pre_end1][v2] + EC[task2] + SHORTEST_DIS[u2][next_start1] - rc1
if rcc1 < cc1:
selected_task2 = (v2, u2)
cc1 = rcc1
# new_solution.costs[sai1] += cc1
new_solution.total_cost += cc1
new_solution.loads[sai1] += ED[task2] - ED[task1]
sa1.insert(sti1, selected_task2)
# second arc cost change : insert task1 into arc2
rc2 = SHORTEST_DIS[pre_end2][u2] + EC[task2] + SHORTEST_DIS[v2][next_start2]
cc2 = SHORTEST_DIS[pre_end2][u1] + EC[task1] + SHORTEST_DIS[v1][next_start2] - rc2
rcc2 = SHORTEST_DIS[pre_end2][v1] + EC[task1] + SHORTEST_DIS[u1][next_start2] - rc2
if rcc2 < cc2:
selected_task1 = (v1, u1)
cc2 = rcc2
# new_solution.costs[sai2] += cc2
new_solution.total_cost += cc2
new_solution.loads[sai2] += ED[task1] - ED[task2]
sa2.insert(sti2, selected_task1)
if sai1 == sai2:
new_solution.total_cost = cal_cost(new_solution.routes, REDGE, ED, EC, SHORTEST_DIS, CAPACITY, DEPOT)
# b, _ = cal_cost1(new_solution.routes, REDGE, ED, EC, SHORTEST_DIS, CAPACITY, DEPOT)
# if (sum(new_solution.costs)-sum(solution.costs))!=(new_solution.total_cost-solution.total_cost):
# print(sai1==sai2)
new_solution.init_valid()
# if sum(solution.costs)== solution.total_cost:
# print(solution.is_valid)
# if cal_cost(new_solution.routes, REDGE, ED, EC, SHORTEST_DIS, CAPACITY, DEPOT)!=new_solution.total_cost:
# print("sb")
# if cal_cost(new_solution.routes, REDGE, ED, EC, SHORTEST_DIS, CAPACITY, DEPOT)!=sum(new_solution.costs):
# print("sb11111111111111111111111111")
return new_solution
def opt(solution: Solution, REDGE, ED, EC, SHORTEST_DIS, CAPACITY, DEPOT, VEHICLE):
new_solution = copy.deepcopy(solution)
routes: list = new_solution.routes
sai = random.randrange(0, len(routes)) # start <= N < end
sa = routes[sai]
sti = random.randrange(0, len(sa)) # start <= N < end
# information used in calculation
u, v = sa[sti]
task = (u, v)
pre_end = sa[sti - 1][1] if sti != 0 else DEPOT
next_start = sa[sti + 1][0] if sti != len(sa) - 1 else DEPOT
new_solution.total_cost += (SHORTEST_DIS[pre_end][v] - SHORTEST_DIS[pre_end][u] + SHORTEST_DIS[u][next_start] -
SHORTEST_DIS[v][next_start])
sa[sti] = (v, u)
new_solution.init_valid()
# if cal_cost(new_solution.routes, REDGE, ED, EC, SHORTEST_DIS, CAPACITY, DEPOT)!=new_solution.total_cost:
# print("sb11111111111111111111111111")
return new_solution
def MS(solution, REDGE, ED, EC, SHORTEST_DIS, CAPACITY, DEPOT, VEHICLE):
new_solution: Solution = copy.deepcopy(solution)
routes: list = new_solution.routes
while (True):
# if random.random()>ALPHA:
# verhicles = random.randrange(1, int(len(routes)/2))
# else:
vehicles = random.randrange(1, int(len(routes)))
verhicles1 = vehicles
# if verhicles>5:
# if random.random() > 0.3:
# verhicles = int(verhicles / 3)
if verhicles1 > 3:
if random.random() > 0.1:
vehicles = int(verhicles1 / 2)
# if verhicles1>5:
# if random.random() > 0.2:
# verhicles = int(verhicles1 / 3)
vehicle = random.sample(range(0, len(routes)), vehicles)
arcs = []
for i in range(vehicles):
sai = vehicle[i]
sa = routes[sai]
arcs.extend(sa)
arct = arcs.copy()
for ob in arct:
arcs.append(inverseArc(ob))
tempt_solution = RPSH(arcs, ED, EC, SHORTEST_DIS, CAPACITY, DEPOT)
# if len(tempt_solution.routes) <= vehicles+VEHICLE-len(solution.routes):
if len(tempt_solution.routes) <= vehicles:
break
vehicle = sorted(vehicle, reverse=True)
for ob in vehicle:
del new_solution.routes[ob]
del new_solution.loads[ob]
for i in range(len(tempt_solution.routes)):
new_solution.routes.append(tempt_solution.routes[i])
new_solution.loads.append(tempt_solution.loads[i])
# new_solution.costs[vehicle[i]] = tempt_solution.costs[i]
new_solution.total_cost = cal_cost(new_solution.routes, REDGE, ED, EC, SHORTEST_DIS, CAPACITY, DEPOT)
# print(new_solution.costs[vehicle[0]])
# a, u = cal_cost1(new_solution.routes, REDGE, ED, EC, SHORTEST_DIS, CAPACITY, DEPOT)
# # uu=new_solution.costs
# if a != new_solution.total_cost:
# print("sb")
new_solution.init_valid()
# print(cal_cost(new_solution.routes, REDGE, ED, EC, SHORTEST_DIS, CAPACITY, DEPOT) )
return new_solution
def searching(population, REDGE, ED, EC, SHORTEST_DIS, CAPACITY, DEPOT, SEED, POPULATION_SIZE, end, VEHICLE):
# random.seed(SEED)
while True:
best = search(population, REDGE, ED, EC, SHORTEST_DIS, CAPACITY, DEPOT, POPULATION_SIZE, VEHICLE)
# if SEED == 1001:
# print(SEED, best[0].total_cost)
if time.time() > end:
break
return best
def search(population, REDGE, ED, EC, SHORTEST_DIS, CAPACITY, DEPOT, POPULATION_SIZE, VEHICLE):
popu = population.copy()
for individual in popu:
if random.random() > individual.discard_prop:
if random.random() > ALPHA:
population.add(local_search(individual, REDGE, ED, EC, SHORTEST_DIS, CAPACITY, DEPOT, VEHICLE))
else:
population.remove(individual)
while len(population) > POPULATION_SIZE:
worst_individual = max(population, key=lambda x: x.total_cost)
population.remove(worst_individual)
valid_population = [p for p in population if p.is_valid]
# print(len(100*valid_population)/len(population))
# if len(valid_population)==0:
# print(len(population))
best_individual = min(valid_population, key=lambda x: x.total_cost)
return best_individual, population
def local_search(solution: Solution, REDGE, ED, EC, SHORTEST_DIS, CAPACITY, DEPOT, VEHICLE):
new_solution = False
while not new_solution:
# [single_insertion, double_insertion, swap,opt]
if solution.is_valid:
new_solution = min([move(solution, REDGE, ED, EC, SHORTEST_DIS, CAPACITY, DEPOT, VEHICLE) for move in
[single_insertion, double_insertion, swap, MS]],
key=lambda x: x.total_cost)
else:
new_solution = min([move(solution, REDGE, ED, EC, SHORTEST_DIS, CAPACITY, DEPOT, VEHICLE) for move in
[single_insertion, double_insertion, swap]],
key=lambda x: x.total_cost)
discard_prop = 0 if new_solution.is_valid else 0.6
if random.random() < discard_prop:
new_solution = False
return new_solution
def command_line(argv):
file_name = argv[0]
termination = int(argv[2])
seed = int(argv[4])
return file_name, termination, seed
#
# def extend_search(pop, end):
# result = []
# # end1 = time.time() + TIME
# # while end1 < end:
# # pool = multiprocessing.Pool()
# # for i in range(CPU):
# # result.append(
# # (pool.apply_async(searching,
# # args=(pop[i], REDGE, ED, EC, SHORTEST_DIS, CAPACITY, DEPOT, SEED + i, POPULATION_SIZE,
# # end1,))))
# # pool.close()
# # pool.join()
# # bests = []
# # for i in result:
# # bests.extend(list(i.get()[1]))
# # random.shuffle(bests)
# # m = int(len(bests) / CPU)
# # pop = []
# # for i in range(0, len(bests), m):
# # pop.append(set(bests[i:i + m]))
# # end1 += TIME
# pool = multiprocessing.Pool()
# for i in range(CPU):
# result.append(
# (pool.apply_async(searching,
# args=(
# pop[i], REDGE, ED, EC, SHORTEST_DIS, CAPACITY, DEPOT, SEED + i, POPULATION_SIZE,
# end,))))
# pool.close()
# pool.join()
# bests = []
# for i in result:
# bests.append(i.get()[0])
# best_individual = min(bests, key=lambda x: x.total_cost)
# return best_individual
def ini_ps(end):
pool = multiprocessing.Pool()
result = []
num = int(init_population)
population_size = int(POPULATION_SIZE)
for i in range(CPU):
result.append((pool.apply_async(population_init, args=(
REDGE, ED, EC, SHORTEST_DIS, num, CAPACITY, DEPOT, SEED + i, end, population_size, VEHICLES,))))
pool.close()
pool.join()
bests = []
for i in result:
bests.append(i.get()[0])
best_individual = min(bests, key=lambda x: x.total_cost)
# sp = []
# for ind in result:
# sp.append(ind.get())
return best_individual
if __name__ == "__main__":
start = time.time()
global SEED
file_name, termination, SEED = command_line(sys.argv[1:])
random.seed(SEED)
set_opt(file_name)
init_path()
end = start + termination - 0.5
best = ini_ps(end)
# best = extend_search(pop, end)
result = ""
routes = []
for i in best.routes:
routes += [0] + i + [0]
for item in routes:
result += str(item) + ","
result = result.replace(' ', '')
print("s", result[:-1])
print("q " + str(best.total_cost))
un_time = (time.time() - start)