Approach for improving reliability in optimal network design

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This paper present an optimal artificial neural network approach for improving upper bound on link reliability in optimal network design. Improving reliability in a growing variable-sized network is an important parameter for optimal network design. Many alternative methods for improving reliability have been used for optimal network design. Most of these methods, mentioned in the literature are simulation-based. These methods provide simple ways for measuring reliability when networks have limited size. These methods require significant computational effort and time for growing variable-sized networks. An optimal neural network method is therefore proposed for reliability improvement in optimal network design. The proposed algorithm has two phases: experimental setup and optimal phase. Experimental setup phase scans all possible network topologies for reliability measures. And, optimal phase constructs optimal network design with improved reliability upper-bound. Both neural networks were studied with fixed and varying links. Results are grouped using cross-validation method showing that the optimised artificial neural network approach gives precise measures for significant reliability improvement than the upper-bound than heuristic-based approach. Results show that the optimised ANN produces optimal network designs and reliability measures at reasonable computational cost.