الفهرس 
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Abstract
The reliability of a network is the probability of performing its function properly. Practical network model could be very large and complex which make the reliability evaluation to become difficult with traditional algorithms, so it is needed to design more efficient techniques for evaluating and optimizing the reliability of the network. This thesis proposes an algorithm which is useful to evaluate some commonly used reliability measures: g-terminal, 2-terminal, and k-terminal reliability by generating the network node sets. from these node sets, depending on the reliability measure which contains at least one node from the specified node set, these selected node sets form minimal cut sets. These minimal cut sets are used as inputs to a multi-variable inversion based sum of disjoint product approach to obtain the unreliability value thereafter. Some approximations in cut sets enumeration and reliability expression terms are developed. These approximations are very useful in large and complex networks. By solving moderate and large networks, the present approach is significantly faster. These minimal cut sets also are used as inputs to the simulated annealing (SA) which is a stochastic and robust meta-heuristic optimization technique to solve the reliability optimization problem which is a mathematical formulated for optimizing the reliability of the network under a set of constraints. The reliability of the network can be optimized by through redundancy allocation by selection of highly reliable edges but with limited overall budgets.