禁忌搜尋演算法

禁忌搜尋（tabu search）演算法是一種亞啟發式(meta-heuristic)隨機搜尋演算法，它從乙個初始可行解出發，選擇一系列的特定搜尋方向（移動）作為試探，選擇實現讓特定的目標函式值變化最多的移動。為了避免陷入區域性最優解，ts採用了一種靈活的「記憶」技術，對已經進行的優化過程進行記錄和選擇，指導下一步的搜尋方向，這就是tabu表的建立。

禁忌搜尋演算法的步驟如下：

1.選定乙個初始解x，給定乙個禁忌表h。

2.若滿足停止條件，則停止計算；否則，在x的鄰域中選出滿足禁忌要求(當某個解足夠好的時候，可以打破這個禁忌準則)的候選集canx，在canx中選擇乙個評價最佳的解xbest，令x=xbest，更新禁忌表h，重複步驟2。

用ts解tsp問題的**如下：

#禁忌搜尋演算法解tsp問題
import numpy as np
import random as rd
import copy
defdiscal
(path)
:    dis =
0for i in
range
(len
(path)-1
):dis += distmat[path[i]
][path[i+1]
]    dis += distmat[path[0]
][path[-1
]]return dis
defswap2
(sol)
:    neighbor =
for i in
range
(len
(sol)):
for j in
range
(i +1,
len(sol)):
s = copy.deepcopy(sol)
x = s[j]
s[j]
= s[i]
s[i]
= xreturn neighbor
distmat = np.array([[
0,350,
290,
670,
600,
500,
660,
440,
720,
410,
480,
970],[
350,0,
340,
360,
280,
375,
555,
490,
785,
760,
700,
1100],
[290
,340,0
,580
,410
,630
,795
,680
,1030
,695
,780
,1300],
[670
,360
,580,0
,260
,380
,610
,805
,870
,1100
,1000
,1100],
[600
,280
,410
,260,0
,610
,780
,735
,1030
,1000
,960
,1300],
[500
,375
,630
,380
,610,0
,160
,645
,500
,950
,815
,950],
[660
,555
,795
,610
,780
,160,0
,495
,345
,820
,680
,830],
[440
,490
,680
,805
,735
,645
,495,0
,350
,435
,300
,625],
[720
,785
,1030
,870
,1030
,500
,345
,350,0
,475
,320
,485],
[410
,760
,695
,1100
,1000
,950
,820
,435
,475,0
,265
,745],
[480
,700
,780
,1000
,960
,815
,680
,300
,320
,265,0
,585],
[970
,1100
,1300
,1100
,1300
,950
,830
,625
,485
,745
,585,0
]])if __name__ ==
'__main__'
:    solution =
[i for i in
range
(distmat.shape[0]
)]rd.shuffle(solution)
#隨機產生初始解
shortestdistance = discal(solution)
tabulist =
#禁忌表
iterx, iterxmax =0,
50while iterx < iterxmax:
cansolution = swap2(solution)
k =shorterdistance =
float
("inf"
)for i in
range
(len
(cansolution)):
if discal(cansolution[i]
)< shorterdistance:
shorterdistance = discal(cansolution[i]
)for i in
range
(len
(k))
:if discal(cansolution[k[
len(k)
-i-1]]
)< shortestdistance:
shortestdistance = discal(cansolution[k[
len(k)
-i-1]]
)                solution = cansolution[k[
len(k)
-i-1]]
break
else
:if cansolution[k[
len(k)
-i-1]]
notin tabulist:
solution = cansolution[k[
len(k)
-i-1]]
break
iflen
(tabulist)
<6:
else
:            tabulist.pop(0)
iterx +=
1print
(solution)
print
(discal(solution)
)

禁忌搜尋演算法

禁忌搜尋演算法

初識禁忌搜尋演算法

禁忌搜尋演算法求解TSP

禁忌搜尋演算法

禁忌搜尋演算法

初識禁忌搜尋演算法

禁忌搜尋演算法求解TSP

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