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Estimating AllTerminal Network Reliability Using a Neural Network
"... The exact calculation of allterminal network reliability is an NPhard problem, with computational effort growing exponentially with the number of nodes and links in the network. Because of the impracticality of calculating allterminal network reliability for networks of moderate to large size, Mon ..."
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Cited by 10 (3 self)
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The exact calculation of allterminal network reliability is an NPhard problem, with computational effort growing exponentially with the number of nodes and links in the network. Because of the impracticality of calculating allterminal network reliability for networks of moderate to large size, Monte Carlo simulation methods to estimate network reliability and upper and lower bounds to bound reliability have been used as alternatives. This paper puts forth another alternative to the estimation of allterminal network reliability that of artificial neural network predictive models. Neural networks are constructed, trained and validated using alternative network topologies, a network reliability upper bound and the exact network reliability as a target. A hierarchical approach is used: a general neural network screens all network designs for reliability followed by a specialized neural network for highly reliable network designs. Results on a ten node problem are given using a grouped cross validation approach.
Designing Reliable Communication Networks with a Genetic Algorithm Using a Repair Heuristic
, 2003
"... This paper investigates GA approaches for solving the reliable communication network design problem. For solving this problem a graph with minimum cost must be found that satisfies a given network reliability constraint. To consider the additional reliability constraint different approaches are poss ..."
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Cited by 5 (1 self)
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This paper investigates GA approaches for solving the reliable communication network design problem. For solving this problem a graph with minimum cost must be found that satisfies a given network reliability constraint. To consider the additional reliability constraint different approaches are possible. We show that existing approaches using penalty functions can result in invalid solutions and are therefore not appropriate for solving this problem. To overcome these problems we present a repair heuristic, which is based on the number of spanning trees in a graph. This heuristic always generates a valid solution, which when compared to a greedy cheapest repair heuristic shows that the new approach finds better solutions with less computational effort.
A Monte Carlo Method for Estimating the Extended AllTerminal Reliability
 Fourth International Conference on Networking and Services, 2008 March 1621
"... Designing a network with optimal deployment cost and maximum reliability considerations is a hard problem, especially when the allterminal reliability is required. For efficiently finding out an acceptable solution, Genetic Algorithms (GAs) have been widely applied to solve this problem. In these G ..."
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Cited by 2 (0 self)
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Designing a network with optimal deployment cost and maximum reliability considerations is a hard problem, especially when the allterminal reliability is required. For efficiently finding out an acceptable solution, Genetic Algorithms (GAs) have been widely applied to solve this problem. In these GAs, the reliability values could be calculated in their objective functions. In year 2002, an extended network reliability model was proposed which considers the connection important level between each pair of nodes. This paper proposes an approximation algorithm based on Monte Carlo simulation for the new network reliability model. This approximation algorithm can be integrated into GAs to solve the optimal cost reliable network design problem under the extended model.
Fast Reliability Search in Uncertain Graphs
"... Uncertain, or probabilistic, graphs have been increasingly used to represent noisy linked data in many emerging application scenarios, and have recently attracted the attention of the database research community. A fundamental problem on uncertain graphs is reliability, which deals with the probab ..."
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Uncertain, or probabilistic, graphs have been increasingly used to represent noisy linked data in many emerging application scenarios, and have recently attracted the attention of the database research community. A fundamental problem on uncertain graphs is reliability, which deals with the probability of nodes being reachable one from another. Existing literature has exclusively focused on reliability detection, which asks to compute the probability that two given nodes are connected. In this paper we study reliability search on uncertain graphs, which we define as the problem of computing all nodes reachable from a set of query nodes with probability no less than a given threshold. Existing reliabilitydetection approaches are not wellsuited to efficiently handle the reliabilitysearch problem. We propose RQtree, a novel index which is based on a hierarchical clustering of the nodes in the graph, and further optimized using a balancedminimumcut criterion. Based on RQtree, we define a fast filteringandverification online queryevaluation strategy that relies on a maximumflowbased candidategeneration phase, followed by a verification phase consisting of either a lowerbounding method or a sampling technique. The first verification method returns no incorrect nodes, thus guaranteeing perfect precision, completely avoids sampling, and is more efficient. The second verification method ensures instead better recall. Extensive experiments on realworld uncertain graphs show that our methods are very efficient—over stateoftheart reliabilitydetection methods, we obtain a speedup up to five orders of magnitude; as well as accurate—our techniques achieve precision> 0.95 and recall usually higher than 0.75. 1.
Scaled Evolutionary Development of Topological Network Designs through Specialised Genetic Operators
, 2003
"... The network design problem can be defined as the search for optimal topological configurations of links connecting a set of fixed nodes. Most of the previous work on the subject has been restricted to small networks of fewer than 20 nodes due to processing and memory requirements as well as the huge ..."
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The network design problem can be defined as the search for optimal topological configurations of links connecting a set of fixed nodes. Most of the previous work on the subject has been restricted to small networks of fewer than 20 nodes due to processing and memory requirements as well as the huge increase in complexity seen as network size is increased. This project explores scaling a genetic algorithm to adapt to solving the problem for much larger networks of up to 200 nodes while still obtaining optimal or nearoptimal solutions for all node configurations. A variety of techniques are used, including specialised genetic operators and heuristics, to improve both the efficiency and performance of the GA in developing solutions to the increasingly complex problems.
Fast Reliability Search in Uncertain Graphs
"... Uncertain, or probabilistic, graphs have been increasingly used to represent noisy linked data in many emerging application scenarios, and have recently attracted the attention of the database research community. A fundamental problem on uncertain graphs is reliability, which deals with the probab ..."
Abstract
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Uncertain, or probabilistic, graphs have been increasingly used to represent noisy linked data in many emerging application scenarios, and have recently attracted the attention of the database research community. A fundamental problem on uncertain graphs is reliability, which deals with the probability of nodes being reachable one from another. Existing literature has exclusively focused on reliability detection, which asks to compute the probability that two given nodes are connected. In this paper we study reliability search on uncertain graphs, which we define as the problem of computing all nodes reachable from a set of query nodes with probability no less than a given threshold. Existing reliabilitydetection approaches are not wellsuited to efficiently handle the reliabilitysearch problem. We propose RQtree, a novel index which is based on a hierarchical clustering of the nodes in the graph, and further optimized using a balancedminimumcut criterion. Based on RQtree, we define a fast filteringandverification online queryevaluation strategy that relies on a maximumflowbased candidategeneration phase, followed by a verification phase consisting of either a lowerbounding method or a sampling technique. The first verification method returns no incorrect nodes, thus guaranteeing perfect precision, completely avoids sampling, and is more efficient. The second verification method ensures instead better recall. Extensive experiments on realworld uncertain graphs show that our methods are very efficient—over stateoftheart reliabilitydetection methods, we obtain a speedup up to five orders of magnitude; as well as accurate—our techniques achieve precision> 0.95 and recall usually higher than 0.75. 1.
Evolutionary Methods for Design of Reliable Networks a chapter in Telecommunications Optimisation: Heuristic and Adaptive Methods
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Reliable Communication Network Design with Evolutionary Algorithms
, 2004
"... For the reliable communication network design (RCND) problem links are unreliable and for each link several options are available with different reliabilities and costs. The goal is to find a costminimal communication network design that satisfies a predefined overall reliability constraint. This ..."
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For the reliable communication network design (RCND) problem links are unreliable and for each link several options are available with different reliabilities and costs. The goal is to find a costminimal communication network design that satisfies a predefined overall reliability constraint. This paper presents two new EA approaches, LaBORNet and BaBORNet, for the RCND problem. LaBORNet uses an encoding that represents the network topology as well as the used link options and repairs infeasible solutions using an additional repair heuristic (CURE). BaBORNet encodes only the network topology and determines the link options by using the repair heuristic CURE as a local search method. The experimental results show that the new EA approaches using repair heuristics outperform existing EA approaches from the literature using penalties for infeasible solutions and find better solutions for existing problems from the literature as well as for new and larger test problems. 1