Network reliability optimization problem of interconnection network under node-edge failure model

The network reliability optimization problem for an interconnection network is to maximize the network reliability subjected to some constraints such as the total cost of the network. Even though, the problem is NP-Hard, many researchers have solved this problem in different ways but with a common assumption that nodes are perfect. But, this assumption is quite unrealistic in nature. In this paper, a new method based on artificial neural network is proposed to solve the network reliability optimization problem considering both the nodes and links of the interconnection networks to be imperfect. The problem is mapped onto an artificial neural network by constructing an energy function whose minimization process drives the neural network into one of its stable states. This stable state corresponds to a solution for the network reliability problem. Some existing methods are studied and compared with proposed method in evaluating the network reliability of some fully connected networks. The comparison reports the proposed method to be better than its counterparts in maximizing the network reliability. The proposed method is used to maximize the reliability of few fully connected networks subjected to some predefined total cost, where the node as well as the links of the networks may fail. Further, the behaviors of the cost as well as the time on the network reliability are discussed.

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