A Universal Concept for Robust Solving of Shortest Path Problems in Dynamically Reconfigurable Graphs
Table 4
Results of the shortest path detection (in Figure 12) using the NAOP-simulator and Benchmarking between the NAOP paradigm and the DVHNN concept. A scenario corresponds to a specific choice of source-destination pair.
Shortest path results using NAOP-simulator
NAOP versus DVHNN
From source to destination
Sim. time (sim)
Convergence
Edges in the shortest path
Total cost of the path
NAOP (ms)
VDHNN (ms)
NAOP
DVHNN
Small weights values: the cost of an edge with index “” is “”
0.1
0.45
48.4
Yes
Yes
,
0.4
1.73
114
Yes
Yes
, , and
0.8
42.2
—
Yes
No
0.3
1
77.3
Yes
Yes
0.7
37.9
—
Yes
No
,
0.5
7.6
89.2
Yes
Yes
0.2
0.94
68.9
Yes
Yes
High weights values: the cost of an edge with index “” is “”
1000
0.54
—
Yes
No
,
4000
0.77
—
Yes
No
, , and
8000
70.3
—
Yes
No
3000
0.22
—
Yes
No
7000
85.4
—
Yes
No
,
5000
0.19
—
Yes
No
2000
0.16
—
Yes
No
In this paper, the concepts have been all implemented in Matlab on a standard PC.