HybridHAM: A Novel Hybrid Heuristic for Finding Hamiltonian Cycle.
1. IntroductionThe Hamiltonian Cycle Problem (HCP) is to identify a cycle in an undirected graph connecting all the vertices in the graph. It is considered as a subproblem of the most popular NP-complete problem, the Travelling Salesman Problem (TSP), where the problem is to find the minimum weighted Hamiltonian cycle. Hamiltonian cycles have many applications like reconstructing genome sequences, solving games like Icosian game, finding a knight's tour on a chessboard, and finding circular embeddings for regular graphs. There is no single efficient algorithm for this problem till date. The state-of-the-art algorithms are mainly classified into two: exponential time exhaustive search algorithms and polynomial time heuristic algorithms. While the first category guarantees giving solution, the latter does not. The latter gives solution in sufficiently less time in most of the cases compared to the first. The algorithms in the first category generally find some efficient pruning rules for reducing the search space and thus improving the running time, while those in the second category find some general thump rules for finding the solution in as many problem instances as possible, in less time. The objective of this research is to design a heuristic which is quicker than the established sophisticated heuristics, but which is more reliable than the very fast techniques.
Many theorems can be found in literature, giving the necessary and sufficient conditions [1-4] for Hamiltonian cycle. These conditions are used for checking whether the graph is Hamiltonian or not. A good study [5] on these theorems and algorithms for solving HCP is given by Vandegriend & Basil. Rubin & Frank [6] proposed an exhaustive search method for finding all Hamiltonian paths or cycles in a directed or undirected graph. Christophides [7] proposed a multipath algorithm that was again an intelligent search algorithm with exponential complexity. Christophides algorithm has been improved by Kocay & William [8] by proposing two operations for pruning the search space. Martello [9] proposed a backtrack search algorithm that uses low degree first heuristic for selecting the next vertex. Ejov et al [10] solved HCP by solving equations drawn from the adjacent matrix of the graph. Even though they could find long cycles in case of non-Hamiltonian graphs, they could not reduce the exponential time complexity. Posa's algorithm [11] is considered as the base algorithm for the heuristic algorithms for HCP. Posa's idea of rotational transformation and its variants formed the basis of almost all heuristic algorithms proposed later. Angluin and Valiant [12] proposed a much more complicated transformation for directed graph, as the rotational transformation is not suitable for directed graph. Bollobas et al. [13] proposed a Hamiltonian cycle algorithm called HAM that uses rotational transformation and cycle extension. Various versions of HAM algorithm like SparseHam [14] and HideHam [15] are also proposed for different kinds of graphs. Brunacci [16] proposed two algorithms DB2 and DB2A, which consider the HCP as a version of TSP and the nonedges as highly weighted edges. DB2A algorithm is a modification of DBA algorithm for directed graphs where directed graph is transformed into undirected graph. Many TSP heuristics like the famous Lin-Kernighan heuristic [17, 18] use a technique called "k-opt" transformation [19, 20], which is an exchange of k edges. Baniasadi et al. [21] proposed a "snakes and ladders" heuristic for solving HCP inspired from k-opt transformations. There are very few published HCP heuristics that sit in the "middle" area between sophisticated reliable heuristics and very simplistic (usually linear or quadratic time) approaches.
The proposed heuristic is a combination of greedy depth first search, unreachable vertex, and rotational transformation heuristics. The greedy depth first search reduces the running time considerably. The search is greedy since it always selects the unvisited low degree vertex for extending the path. While the unreachable vertex heuristic reduces the chances of reaching dead end, the rotational transformation helps to come out of the dead end condition. Thus the proposed method is faster than the complex exact algorithms and more reliable than the faster heuristics.
2. Materials and Methods
2.1. Hamiltonian Cycle Problem. Hamiltonian cycle is a cycle connecting all the vertices in a given graph only once. A graph containing at least one Hamiltonian cycle is called Hamiltonian graph. This optimization problem can be formally defined as follows:
Given a graph G = (E, V), where E is the set of edges of the graph, V is the set of vertices of the graph and [absolute value of V] = n.
The problem is to find a cycle, HC = ([v.sub.1], [v.sub.2], ..., [v.sub.n]), where all ([v.sub.1], [v.sub.i+1]) and ([v.sub.1], [v.sub.n]) are elements of E.
It is a hard problem that attracted both mathematicians and computer scientists. It is considered as one of the strong representatives of the NP-complete problem.
2.2. Greedy Depth First Search. The large search space of the HCP can be explored either breadth wise or depth wise. In depth first search, search is performed in depth wise manner starting from a given vertex till the point where the search cannot proceed further or a dead end is reached. The proposed heuristic uses greedy depth first search to create a Hamiltonian path. The path construction starts from the highest degree vertex because it increases the probability of returning to the starting vertex. Degree of a vertex is the number of edges connected to that vertex. The other vertices of the path are selected greedily by selecting the smallest degree neighbour among the unvisited neighbours. The lowest degree vertices are added first into the path since they may become unreachable with the selection of its neighbours. For example, if the degree of a vertex is two then both the edges of that vertex must be present in the Hamiltonian path/cycle and these edges are added to the path first. The vertices that are part of the path constructed so far are considered as "visited." Since in Hamiltonian cycle a vertex appears only once, only the unvisited neighbours of the current vertex need to be considered. In order to guide the greedy depth first search further to longer paths, an unreachable vertex heuristic is proposed. According to this heuristic, before adding a vertex to the path, an unreachable condition is checked for each of its neighbours. This heuristic is explained in Section 2.3.
2.3. Unreachable Vertex Heuristic. This research proposes a new heuristic, namely, unreachable vertex heuristic, to reduce the chance of reaching a dead end while constructing the Hamiltonian path.
Definition 1. Let P be the partial path and x be the end vertex on partial path. Select the next vertex y [member of] Adj(x) in the path such that the number of unvisited adjacent vertices of all Adj(y) vertices is greater than one.
A vertex is said to be unreachable if all its neighbours are already a part of the path constructed so far. In this case, there is no way to reach the vertex and it becomes unreachable. In this proposed heuristic, if the selection of a vertex is making any of its unvisited neighbours unreachable then that vertex will not be added to path.
For example, consider the graph shown in Figure 1.
Let the partial path identified so far be A [right arrow] C [right arrow] D. In the given graph Adj(D) = {B, E}. Let the next vertex selected be E. Adj(E) = {B, G, F}. Number of unvisited neighbours of B, G and F are 1,2 and 2 respectively. According to the unreachable vertex heuristic, E will not be selected as next vertex in the path since the number of unvisited neighbours of B is one.
2.4. Rotational Transformation. Rotational transformation [11] and its variations are found to be powerful heuristics for finding Hamiltonian cycle. It is used to change a path to get a new end vertex as shown in Figure 2. In the figure, e is the end on which rotational transformation is applied to get a new end vertex.
The procedure of rotational transformation is summarized below.
(1) Find a vertex adjacent to e in the input graph, in path P. Let it be vertex b.
(2) Create a new path P',
(a) by connecting b to e
(b) by reversing the path from c to e
In the proposed algorithm, rotational transformation is used for two purposes.
(1) To come out of the dead end during the construction of Hamiltonian path.
(2) To convert the Hamiltonian path into Hamiltonian cycle.
In the first case highest degree end of the path is selected for rotational transformation since it increases the probability of getting a new end. Similarly for converting path into cycle, the lowest degree end vertex of the path is selected for rotational transformation since it increases the probability of returning to the higher degree end to make the cycle.
3. Proposed Hybrid Heuristic
3.1. HybridHAM Algorithm. The proposed HybridHAM algorithm works in three steps:
(1) Create an initial path
(2) Convert the initial path to Hamiltonian path.
(3) Convert the Hamiltonian path to Hamiltonian cycle.
3.1.1. Initial Path Creation. The path creation starts from the highest degree vertex and then adds the smallest degree vertices greedily in order to construct an initial path as long as possible. The selection of highest degree initial vertex and then the smallest degree vertices is meant for constructing a longer initial path and is given in Table 1. The algorithm checks the unreachable condition before adding a vertex in the path. This is to reduce the probability of reaching a dead end during the path construction. If there are more than one highest degree vertices then repeat this procedure of creating initial path by selecting each one of them as the starting vertex and select the path with maximum number of vertices as the initial path. By following this procedure, we may get an initial path which is Hamiltonian or near Hamiltonian.
3.1.2. Conversion of Initial Path into Hamiltonian Path. If the initial path created is not Hamiltonian (less number of vertices than the total number of vertices), then select the highest degree end of the initial path for rotational transformation. This is to increase the probability for getting a new end vertex for extending the path further to create a Hamiltonian path. Extend the path from the new end vertex by following the greedy procedure in Section 3.1.1. The rotational transformation and greedy path extension are continued until we are getting a Hamiltonian path. At any time during this process, rotational transformation could not be applied means that either the graph is not having Hamiltonian path or the algorithm fails to identify the Hamiltonian path.
3.1.3. Conversion of Hamiltonian Path into Hamiltonian Cycle. If there is an edge connecting the two ends of the Hamiltonian path in the graph then add that edge to the path to get the Hamiltonian cycle. Otherwise, apply rotational transformation to the smallest degree vertex repeatedly until getting a new end vertex which can be connected to the other end vertex to form the Hamiltonian cycle. At any time during this process, rotational transformation could not be applied, means that either the graph is not having Hamiltonian cycle or the algorithm fails to identify the Hamiltonian cycle.
The vertex selection criteria used in various steps are summarized in Table 1.
3.2. Example. Consider the undirected graph in Figure 3.
3.2.1. Initial Path Identified in Step 1
0 1 2 3 4 5 6 7 8 12 13 14 22 21 20 19 18 15
Here the length of the initial path is 18, which is less than the total number of vertices in the graph. Since the identified path is not Hamiltonian, go to step 2.
3.2.2. Hamiltonian Path Identified in Step 2
0 7 8 12 14 13 15 17 16 9 1 2 3 4 5 6 11 23 22 21 20 18 19 10
Here the length of the path is 24 and hence it is Hamiltonian path. Since there is no edge in Figure 3, connecting the end vertices of the path (i.e., edge connecting vertex 1 and vertex 11), go to step 3 and apply rotational transformation to find the new end vertices that are connected in the graph in Figure 3
3.2.3. Hamiltonian Cycle Identified in Step 3
0 7 8 12 14 13 15 16 17 18 19 20 21 22 23 11 6 5 4 10 3 2 9 1
Here the end vertices 1 and 2 are connected in the initial graph shown in Figure 3 and hence form the Hamiltonian cycle. It is shown in Figure 4.
3.3. Pseudocode. The input to the algorithm is the adjacency matrix representation of the graph and the output is the set of vertices corresponding to the Hamiltonian cycle. Let n be the number of vertices in the graph. The proposed algorithm contains three phases and is outlined as follows:
HybridHAM()
{
From the input adjacency matrix, create two arrays [V.sub.a] and [V.sub.d] of vertices sorted in the increasing and decreasing order of their degrees, respectively.
//Phase 1
//Create an initial path
(1) Start from one of the highest degree vertex (first vertex in the array [V.sub.d]). Let it be [v.sub.s].
(2) Add it to the initial path [P.sub.i].
(3) Repeat
(a) Select the next smallest degree neighbour of [v.sub.s] (from the array [V.sub.a]). Let it be [v.sub.i].
(b) If the selection of [v.sub.i] is not making any of its unvisited neighbours unreachable then
(i) add it to the initial path [P.sub.i]
(ii) make [v.sub.i] as [v.sub.s]
Until [v.sub.s] becomes a dead end.
//End of Phase 1
(4) If [absolute value of [P.sub.i]] = n then go to Phase 3.
Else
Repeat Phase 1 for each of the highest degree vertex of
the graph and select the
longest [P.sub.i] as initial path.
End if
//Phase 2
//Convert the initial path into Hamiltonian path
(5) Repeat
(a) Select the highest degree end of the path [P.sub.i] for
rotational transformation
(b) Reverse [P.sub.i] if the first vertex in [P.sub.i] is
having degree higher than the last vertex to make the
highest degree vertex as the end vertex of the path. Let
it be [v.sub.x].
(c) Apply rotational transformation to [P.sub.i] using
[v.sub.x] to get a new path.
(d) If rotational transformation could not be
applied then either the graph is not having
Hamiltonian path or the algorithm fails to
identify the Hamiltonian path and so exit.
(e) Extend this new path by using the greedy depth
first search as in Phase 1.
Until [absolute value of [P.sub.i]] = n.
(6) Now [P.sub.i] is the Hamiltonian Path [P.sub.h]. Assign
[P.sub.h] = [P.sub.i].
//End of Phase 2
(7) If there is an edge connecting the first and last vertices
of [P.sub.h] in the graph then
[P.sub.h] is the Hamiltonian cycle and return [P.sub.h]
else
go to Phase 3
End if
//Phase 3
//Convert Hamiltonian path into Hamiltonian cycle
(8) Repeat
(a) Select the smallest degree end of the path [P.sub.h] for
rotational transformation
(b) Reverse [P.sub.h] if the first vertex in [P.sub.h] is
having degree higher than the last vertex to make the
smallest degree vertex as the end vertex of the
path. Let it be [v.sub.y].
(c) Apply rotational transformation to [P.sub.h] using
[v.sub.y] to get a new path.
(d) If rotational transformation could not be
applied then either the graph is not having
Hamiltonian Cycle or the algorithm fails to
identify the Hamiltonian path and so exit.
Until there is an edge connecting the first and last
vertices of the path Ph.
(9) Now [P.sub.h] is the Hamiltonian cycle and return [P.sub.h].
//End of Phase 3
}// End of HybridHAM algorithm
3.4. Worst Case Complexity
(1) Creation of sorted arrays
T(n) = O([n.sup.2])
(2) Phase 1
Greedy depth first search goes through the adjacency matrix once in the worst case and hence its complexity is O([n.sup.2]).
This search is repeated for each of the highest degree vertex. The worst case is all the n vertices are of the same degree.
Therefore, T(n) = O([n.sup.3])
(3) Phase 2
Complexity of rotational transformation at the most is O(n)
Complexity of greedy depth first search is O([n.sup.2])
Total of these two = O([n.sup.2])
These two operations are repeated at most n times.
Therefore, T(n) = O([n.sup.3])
(4) Phase 3
Complexity of rotational transformation at the most is O(n)
This operation is repeated at most n times.
Therefore, T(n) = O([n.sup.2])
Total worst case complexity of HybridHam, T(n) = O([n.sup.2]) + O([n.sup.3]) + O([n.sup.3]) + O([n.sup.2]) = O([n.sup.3]).
4. Experiments
The performance evaluation experiments of the proposed algorithm are conducted in a system with 4GB RAM and Intel Core i5 processor. The algorithm is implemented in MATLAB 13RA. The experiments were carried out on a set of graphs collected from the literature as well as from the TSPLIB.
4.1. Sample Hamiltonian Cycle Graphs. The Hamiltonian cycles returned by the proposed algorithm on the collected set of sample graphs are shown in Figure 5.
The running time of the algorithm to solve these sample instances is given in Table 2.
4.2. TSPLIB Instances. The TSPLIB library [22] contains seven Hamiltonian cycle instances of 1000, 2000, 3000, and 5000 vertices. The proposed algorithm could solve all these problem instances in a few seconds. The running time of the proposed algorithm is compared with that of HCP Solver [23], Concorde TSP Solver [24], and the latest Snake and Ladder Heuristic [21]. Table 3 gives the time taken by these algorithms to solve each of the HCP instances.
4.3. FHCP Challenge Set. The FHCP Challenge Set [25] is a collection of 1001 Hamiltonian Cycle Problem instances. This dataset is specifically designed to resist the heuristic approaches. HybridHAM is tested on 250 instances of the FHCP Challenge Set. Out of these 250 instances, the proposed heuristic could find 75 Hamiltonian paths and 6 Hamiltonian cycles. The complete results are shown in Table 4. The graphs are too sparse so that the rotation transformation could not convert the Hamiltonian path into Hamiltonian cycle in Phase 3 of the algorithm. Phase 1 and Phase 2 of the algorithm perform comparatively well with the highly difficult instances of the Challenge Set and hence the Hamiltonian path. It is also found that the proposed highest degree and smallest degree vertex selection criteria suit more graphs having vertices of different degrees. However, the algorithm could find Hamiltonian cycle from medium sparse graphs in very less time (e.g., Graphs 72, 79, 84, 90, etc. in Table 4). Even the winners [26] of the challenge could not solve the complete set and they used different algorithms for different types of graphs as their objective was to find solutions rather than developing algorithms.
It would be worth pointing out that the difficult instances of FHCP challenge dataset are very rare and difficult to construct, let alone encounter "naturally." Also even discovering a Hamiltonian path is a difficult (NP-complete) problem and the proposed heuristic succeeded in doing so for many instances of the FHCP Challenge Set.
5. Conclusion
This paper proposes a hybrid heuristic for finding Hamiltonian cycle from undirected graphs. The proposed three-stage algorithm uses a combination of three heuristics: greedy depth first search, unreachable vertex, and rotational transformation. From the various experiments conducted on different graphs of variable sizes and complexity, it is found that the highest degree heuristic used for selecting the initial vertex for the creation of longest initial path as well as Hamiltonian path and the smallest degree heuristic used for the conversion of Hamiltonian path to Hamiltonian cycle are the reasons for getting the solution in a single run. The experimental evaluation also shows that the proposed heuristic is much faster and succeeds in obtaining good results in the majority of cases. It should be worth noting that the increase in speed comes without sacrificing the heuristic's reliability on "easy" instances of various sizes (including the quite large TSPLIB instances) and that it still performs reasonably well on more difficult instances.
https://doi.org/10.1155/2018/9328103
Data Availability
The data supporting this research are from previously reported studies and datasets, which have been cited. The processed data are available in [22, 25].
Conflicts of Interest
The author declares that there is no conflict of interest.
Acknowledgments
The research presented in this manuscript is supported by University Grants Commission Major Project Grant F.No.42136/2013(SR).
References
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[19] S. Lin, "Computer solutions of the traveling salesman problem," Bell Labs Technical Journal, vol. 44, pp. 2245-2269, 1965.
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[21] P. Baniasadi, V. Ejov, J. A. Filar, M. Haythorpe, and S. Rossomakhine, "Deterministic "snakes and Ladders" Heuristic for the Hamiltonian cycle problem," Mathematical Programming Computation, vol. 6, no. 1, pp. 55-75, 2014.
[22] TSPLIB-Hamiltonian cycle problem (HCP), accessed on June 2013, http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95.
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[25] M. Haythorpe, "FHCP challenge set: the first set of structurally difficult instances of the hamiltonian cycle problem," Bulletin of the Institute of Combinatorics and Its Applications, vol. 83, pp. 98-107, 2018.
[26] A. Schneider, "When researchers play to solve 1001 instances of the Hamiltonian Cycle Problem," 2016, https://www.inria.fr/en/ centre/sophia/news/when-researchers-play-to-solve-1001-instances-of-the-hamiltonian-cycle-problem.
K. R. Seeja (iD)
Department of Computer Science & Engineering, Indira Gandhi Delhi Technical University for Women, Kashmere Gate, Delhi 110006, India
Correspondence should be addressed to K. R. Seeja; krseeja@gmail.com
Received 9 May 2018; Revised 7 September 2018; Accepted 25 September 2018; Published 16 October 2018
Academic Editor: Jun He
Caption: Figure 1: Unreachable vertex.
Caption: Figure 2: Rotational transformation.
Caption: Figure 3: Input undirected graph.
Caption: Figure 4: Hamiltonian cycle.
Caption: Figure 5: Sample Hamiltonian graphs.
Table 1: Vertex selection criteria.
Vertex Selection Rule Step Reason
Selection of Highest Step 1 It increases the probability of
degree initial vertex. getting a path to reach back to
this vertex to form the cycle.
Greedy selection of Step 1 Giving preference to smaller
smallest degree vertices degree vertices increases the
during path construction. probability of longer paths. For
example, if the degree of a
vertex adjacent to the current
end vertex of the path is two,
then it should be included first
in the path, as it is the only
possible position of the vertex
in the Hamiltonian path.
Selection of the highest Step 2 It increases the probability for
degree end of the initial getting a new end vertex for
path for rotational extending the path further to
transformation create a Hamiltonian path
Selection of the smallest Step 3 This is done to keep the highest
degree end vertex for degree end vertex, since the
rotational transformation probability of getting an edge
connecting a higher degree vertex
is more compared to a smaller
degree vertex.
Table 2: Running time on sample instances.
Sl.No Name No. of Vertices Running time in Sec.
1. Example 1 16 0.051
2. Example 2 20 0.056
3. Example 3 11 0.051
4. Example 4 12 0.054
5. Example 5 11 0.051
6. Example 6 20 0.049
7 Example 7 12 0.052
8. Example 8 24 0.083
9. Example 9 24 0.063
10. Example 10 12 0.051
11. Example 11 6 0.049
12. Example 12 12 0.031
13. Example 13 8 0.029
14. Example 14 12 0.034
15. Example 15 19 0.030
16. Example 16 12 0.032
17. Example 17 13 0.030
18. Example 18 16 0.038
19. Example 19 60 0.043
20. Example 20 20 0.042
21. Example 21 25 0.031
22. Example 22 32 0.032
23. Example 23 64 0.078
Table 3: Running time on TSPLIB instances.
Running time in sec.
Sl.No Name No. of Concorde HCP Snake and Hybrid
vertices Solver Ladder Ham
Heuristic
1. Alb1000 1000 4.95 0.72 0.1 0.2656
2. Alb2000 2000 7.30 3.29 0.8 1.4375
3. Alb3000a 3000 9.56 8.36 3.44 2.7656
4. Alb3000b 3000 9.94 8.02 3.64 1.5781
5. Alb3000c 3000 9.95 8.43 4.31 1.8438
6. Alb3000d 3000 10.14 8.48 4.03 1.6406
7. Alb3000e 3000 10.44 8.03 4.29 1.6719
8. Alb4000 4000 13.45 17.84 13.89 3.0625
9. Alb5000 5000 17.24 30.85 14.12 8.9844
Table 4: Result on FHCP Challenge Set.
Sl.No Name No. of No. of Edges Output Running Time
Vertices in Sec.
1. Graph 1 66 99 HP 0.0625
2. Graph 2 70 106 HC 0.0469
3. Graph 3 78 117 HP 0.0625
4. Graph 4 84 127 HP 0.0625
5. Graph 5 90 135 HP 0.0625
6. Graph 6 94 142 HC 0.0625
7. Graph 7 102 153 HP 0.0781
8. Graph 8 108 162 HP 0.0625
9. Graph 9 114 171 HP 0.0938
10. Graph 11 126 189 HP 0.1094
11. Graph 12 132 199 HP 0.1094
12. Graph 14 142 214 HP 0.0938
13. Graph 15 150 225 HP 0.1250
14. Graph 16 156 235 HP 0.0938
15. Graph 17 162 243 HP 0.1875
16. Graph 18 166 250 HP 0.1094
17. Graph 20 174 261 HP 0.2344
18. Graph 21 180 271 HP 0.1406
19. Graph 22 186 279 HP 0.2188
20. Graph 23 190 286 HP 0.1563
21. Graph 25 204 307 HP 0.1719
22. Graph 26 210 315 HP 0.3750
23. Graph 27 214 322 HP 0.2031
24. Graph 29 228 343 HP 0.2500
25. Graph 32 246 369 HP 0.4063
26. Graph 33 252 379 HP 0.3281
27 Graph 34 258 387 HP 0.4688
28. Graph 35 262 394 HP 0.5781
29. Graph 36 270 405 HP 0.7031
30. Graph 37 276 415 HP 0.3750
31. Graph 40 294 441 HP 1.0469
32. Graph 41 300 451 HP 0.4844
33. Graph 43 310 466 HP 0.7031
34. Graph 44 312 477 HP 0.9844
35. Graph 45 324 487 HP 0.6094
36. Graph 50 348 523 HP 0.9063
37. Graph 53 366 549 HP 1.6875
38. Graph 54 372 559 HP 0.9219
39. Graph 58 396 595 HP 1.7031
40. Graph 59 400 40001 HC 0.2656
41. Graph 64 416 625 HP 1.0938
42. Graph 65 419 631 HP 1.4375
43. Graph 68 438 657 HP 2.9219
44. Graph 69 444 667 HP 2.9063
45. Graph 72 460 52901 HC 0.4375
46. Graph 79 480 57601 HC 0.4844
47. Graph 82 496 745 HP 1.7031
48. Graph 84 500 62501 HC 0.5313
49. Graph 90 510 65026 HC 0.5625
50. Graph 91 516 775 HP 3.5156
51. Graph 95 540 811 HP 3.0469
52. Graph 96 540 72901 HC 0.7031
53. Graph 99 550 826 HP 5.6719
54. Graph 104 576 865 HP 2.8594
55. Graph 118 636 955 HP 6.8594
56. Graph 122 656 985 HP 4.1563
57 Graph 124 660 991 HP 8.2813
58. Graph 128 677 114583 HC 1.3594
59. Graph 134 724 131045 HC 1.5313
60. Graph 137 736 1105 HP 12.9531
61. Graph 148 816 1225 HP 7.9531
62. Graph 150 823 169333 HC 2.7500
63. Graph 151 828 1243 HP 11.5625
64. Graph 160 896 1345 HP 14
65. Graph 162 909 206571 HC 4.0625
66. Graph 168 972 1459 HP 33.0938
67. Graph 169 976 1465 HP 28.6406
68. Graph 176 1020 1531 HP 29.4375
69. Graph 182 1056 1585 HP 22.6719
70. Graph 188 1123 315283 HC 9.6406
71. Graph 190 1136 1705 HP 14.4844
72. Graph 203 1216 1825 HP 40.2813
73. Graph 211 1296 1945 HP 40.1406
74. Graph 233 1456 2185 HP 85.4688
75. Graph 246 1536 2305 HP 111.8438
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| Title Annotation: | Research Article |
|---|---|
| Author: | Seeja, K.R. |
| Publication: | Journal of Optimization |
| Date: | Jan 1, 2018 |
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