![](bg6.png)
parameter density, therefore, it could get an Approx-
Opt-Plan very close to the Opt-Plan by limited
amount of searches in the Plan-Space. The merits of
high-efficiency, controllable, and asymptotically
optimal, ensure that it could make full use of
available computing resource to find possible better
result, therefore very suitable for large-scale network.
7 CONCLUSIONS
In this paper, we proposed the Self-ad-MCNHA-
SLOS to address the MCNH problem using SLOS
and self-adaptive parameter adjust strategy. It could
find an approximate optimal hardening plan close
enough to the optimal hardening plan by limited
amount of searches, and has the merits of high-
efficiency, controllable and asymptotically optimal,
therefore, can make full use of available computing
resource to find possible better result, and is very
suitable for large-scale network environment.
Considering the Self-ad-MCNHA-SLOS’ ability of
transforming NP-hard problem to P-hard iterations,
we will study the generalization of the algorithm to
solve more hard problems in future.
ACKNOWLEDGEMENTS
This paper is supported by the National High
Technology Research and Development Program of
China (863 Program) under Grant
No.2009AA01Z432, the National Natural Science
Foundation of China under Grant No.60873215 and
the Hunan Provincial Natural Science Foundation of
China under Grant No.s2010J5050.
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Self-ad-MCNHA-SLOS - A Self-adaptive Minimum-Cost Network Hardening Algorithm based on Stochastic Loose
Optimize Strategy
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