Search results for: electrical networks.
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 2593

Search results for: electrical networks.

2383 Design and Analysis of Extra High Voltage Non-Ceramic Insulator by Finite Element Method

Authors: M. Nageswara Rao, V. S. N. K. Chaitanya, P. Pratyusha

Abstract:

High voltage insulator has to withstand sever electrical stresses. Higher electrical stresses lead to erosion of the insulator surface. Degradation of insulating properties leads to flashover and in some extreme cases it may cause to puncture. For analyzing these electrical stresses and implement necessary actions to diminish the electrical stresses, numerical methods are best. By minimizing the electrical stresses, reliability of the power system will improve. In this paper electric field intensity at critical regions of 400 kV silicone composite insulator is analyzed using finite element method. Insulator is designed using FEMM-2D software package. Electric Field Analysis (EFA) results are analyzed for five cases i.e., only insulator, insulator with two sides arcing horn, High Voltage (HV) end grading ring, grading ring-arcing horn arrangement and two sides grading ring. These EFA results recommended that two sides grading ring is better for minimization of electrical stresses and improving life span of insulator.

Keywords: Polymer insulator, electric field analysis, numerical methods, finite element method, FEMM-2D.

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2382 Improved Exponential Stability Analysis for Delayed Recurrent Neural Networks

Authors: Miaomiao Yang, Shouming Zhong

Abstract:

This paper studies the problem of exponential stability analysis for recurrent neural networks with time-varying delay.By establishing a suitable augmented LyapunovCKrasovskii function and a novel sufficient condition is obtained to guarantee the exponential stability of the considered system.In order to get a less conservative results of the condition,zero equalities and reciprocally convex approach are employed. The several exponential stability criterion proposed in this paper is simpler and effective. A numerical example is provided to demonstrate the feasibility and effectiveness of our results.

Keywords: Exponential stability , Neural networks, Linear matrix inequality, Lyapunov-Krasovskii, Time-varying.

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2381 An Adversarial Construction of Instability Bounds in LIS Networks

Authors: Dimitrios Koukopoulos

Abstract:

In this work, we study the impact of dynamically changing link slowdowns on the stability properties of packetswitched networks under the Adversarial Queueing Theory framework. Especially, we consider the Adversarial, Quasi-Static Slowdown Queueing Theory model, where each link slowdown may take on values in the two-valued set of integers {1, D} with D > 1 which remain fixed for a long time, under a (w, ¤ü)-adversary. In this framework, we present an innovative systematic construction for the estimation of adversarial injection rate lower bounds, which, if exceeded, cause instability in networks that use the LIS (Longest-in- System) protocol for contention-resolution. In addition, we show that a network that uses the LIS protocol for contention-resolution may result in dropping its instability bound at injection rates ¤ü > 0 when the network size and the high slowdown D take large values. This is the best ever known instability lower bound for LIS networks.

Keywords: Network stability, quality of service, adversarial queueing theory, greedy scheduling protocols.

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2380 Mining Implicit Knowledge to Predict Political Risk by Providing Novel Framework with Using Bayesian Network

Authors: Siavash Asadi Ghajarloo

Abstract:

Nowadays predicting political risk level of country has become a critical issue for investors who intend to achieve accurate information concerning stability of the business environments. Since, most of the times investors are layman and nonprofessional IT personnel; this paper aims to propose a framework named GECR in order to help nonexpert persons to discover political risk stability across time based on the political news and events. To achieve this goal, the Bayesian Networks approach was utilized for 186 political news of Pakistan as sample dataset. Bayesian Networks as an artificial intelligence approach has been employed in presented framework, since this is a powerful technique that can be applied to model uncertain domains. The results showed that our framework along with Bayesian Networks as decision support tool, predicted the political risk level with a high degree of accuracy.

Keywords: Bayesian Networks, Data mining, GECRframework, Predicting political risk.

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2379 Knowledge Management in Cross- Organizational Networks as Illustrated by One of the Largest European ICT Associations A Case Study of the “METORA

Authors: Thomas Klauß

Abstract:

In networks, mainly small and medium-sized businesses benefit from the knowledge, experiences and solutions offered by experts from industry and science or from the exchange with practitioners. Associations which focus, among other things, on networking, information and knowledge transfer and which are interested in supporting such cooperations are especially well suited to provide such networks and the appropriate web platforms. Using METORA as an example – a project developed and run by the Federal Association for Information Economy, Telecommunications and New Media e.V. (BITKOM) for the Federal Ministry of Economics and Technology (BMWi) – This paper will discuss how associations and other network organizations can achieve this task and what conditions they have to consider.

Keywords: Associations, collaboration, communities, crossdepartmental organizations, semantic web, web 2.0.

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2378 Enhancing the Connectedness in Ad–hoc Mesh Networks using the Terranet Technology

Authors: Obeidat I., Bsoul M., Khasawneh A., Kilani Y.

Abstract:

This paper simulates the ad-hoc mesh network in rural areas, where such networks receive great attention due to their cost, since installing the infrastructure for regular networks in these areas is not possible due to the high cost. The distance between the communicating nodes is the most obstacles that the ad-hoc mesh network will face. For example, in Terranet technology, two nodes can communicate if they are only one kilometer far from each other. However, if the distance between them is more than one kilometer, then each node in the ad-hoc mesh networks has to act as a router that forwards the data it receives to other nodes. In this paper, we try to find the critical number of nodes which makes the network fully connected in a particular area, and then propose a method to enhance the intermediate node to accept to be a router to forward the data from the sender to the receiver. Much work was done on technological changes on peer to peer networks, but the focus of this paper will be on another feature which is to find the minimum number of nodes needed for a particular area to be fully connected and then to enhance the users to switch on their phones and accept to work as a router for other nodes. Our method raises the successful calls to 81.5% out of 100% attempt calls.

Keywords: Adjacency matrix, Ad-hoc mesh network, Connectedness, Terranet technology

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2377 Improved Robust Stability Criteria for Discrete-time Neural Networks

Authors: Zixin Liu, Shu Lü, Shouming Zhong, Mao Ye

Abstract:

In this paper, the robust exponential stability problem of uncertain discrete-time recurrent neural networks with timevarying delay is investigated. By constructing a new augmented Lyapunov-Krasovskii function, some new improved stability criteria are obtained in forms of linear matrix inequality (LMI). Compared with some recent results in literature, the conservatism of the new criteria is reduced notably. Two numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed results.

Keywords: Robust exponential stability, delay-dependent stability, discrete-time neutral networks, time-varying delays.

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2376 Computational Networks for Knowledge Representation

Authors: Nhon Van Do

Abstract:

In the artificial intelligence field, knowledge representation and reasoning are important areas for intelligent systems, especially knowledge base systems and expert systems. Knowledge representation Methods has an important role in designing the systems. There have been many models for knowledge such as semantic networks, conceptual graphs, and neural networks. These models are useful tools to design intelligent systems. However, they are not suitable to represent knowledge in the domains of reality applications. In this paper, new models for knowledge representation called computational networks will be presented. They have been used in designing some knowledge base systems in education for solving problems such as the system that supports studying knowledge and solving analytic geometry problems, the program for studying and solving problems in Plane Geometry, the program for solving problems about alternating current in physics.

Keywords: Artificial intelligence, artificial intelligence and education, knowledge engineering, knowledge representation.

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2375 Stability Analysis of Neural Networks with Leakage, Discrete and Distributed Delays

Authors: Qingqing Wang, Baocheng Chen, Shouming Zhong

Abstract:

This paper deals with the problem of stability of neural networks with leakage, discrete and distributed delays. A new Lyapunov functional which contains some new double integral terms are introduced. By using appropriate model transformation that shifts the considered systems into the neutral-type time-delay system, and by making use of some inequality techniques, delay-dependent criteria are developed to guarantee the stability of the considered system. Finally, numerical examples are provided to illustrate the usefulness of the proposed main results.

Keywords: Neural networks, Stability, Time-varying delays, Linear matrix inequality.

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2374 Blockchain Security in MANETs

Authors: Nada Mouchfiq, Ahmed Habbani, Chaimae Benjbara

Abstract:

The security aspect of the IoT occupies a place of great importance especially after the evolution that has known this field lastly because it must take into account the transformations and the new applications .Blockchain is a new technology dedicated to the data sharing. However, this does not work the same way in the different systems with different operating principles. This article will discuss network security using the Blockchain to facilitate the sending of messages and information, enabling the use of new processes and enabling autonomous coordination of devices. To do this, we will discuss proposed solutions to ensure a high level of security in these networks in the work of other researchers. Finally, our article will propose a method of security more adapted to our needs as a team working in the ad hoc networks, this method is based on the principle of the Blockchain and that we named ”MPR Blockchain”.

Keywords: Ad hoc networks, blockchain, MPR, security.

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2373 A Systematic Construction of Instability Bounds in LIS Networks

Authors: Dimitrios Koukopoulos

Abstract:

In this work, we study the impact of dynamically changing link slowdowns on the stability properties of packetswitched networks under the Adversarial Queueing Theory framework. Especially, we consider the Adversarial, Quasi-Static Slowdown Queueing Theory model, where each link slowdown may take on values in the two-valued set of integers {1, D} with D > 1 which remain fixed for a long time, under a (w, p)-adversary. In this framework, we present an innovative systematic construction for the estimation of adversarial injection rate lower bounds, which, if exceeded, cause instability in networks that use the LIS (Longest-in- System) protocol for contention-resolution. In addition, we show that a network that uses the LIS protocol for contention-resolution may result in dropping its instability bound at injection rates p > 0 when the network size and the high slowdown D take large values. This is the best ever known instability lower bound for LIS networks.

Keywords: Parallel computing, network stability, adversarial queuing theory, greedy scheduling protocols.

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2372 A Novel Fuzzy-Neural Based Medical Diagnosis System

Authors: S. Moein, S. A. Monadjemi, P. Moallem

Abstract:

In this paper, application of artificial neural networks in typical disease diagnosis has been investigated. The real procedure of medical diagnosis which usually is employed by physicians was analyzed and converted to a machine implementable format. Then after selecting some symptoms of eight different diseases, a data set contains the information of a few hundreds cases was configured and applied to a MLP neural network. The results of the experiments and also the advantages of using a fuzzy approach were discussed as well. Outcomes suggest the role of effective symptoms selection and the advantages of data fuzzificaton on a neural networks-based automatic medical diagnosis system.

Keywords: Artificial Neural Networks, Fuzzy Logic, MedicalDiagnosis, Symptoms, Fuzzification.

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2371 Evaluating Performance of Quality-of-Service Routing in Large Networks

Authors: V. Narasimha Raghavan, M. Venkatesh, T. Peer Meera Labbai, Praveen Dwarakanath Prabhu

Abstract:

The performance and complexity of QoS routing depends on the complex interaction between a large set of parameters. This paper investigated the scaling properties of source-directed link-state routing in large core networks. The simulation results show that the routing algorithm, network topology, and link cost function each have a significant impact on the probability of successfully routing new connections. The experiments confirm and extend the findings of other studies, and also lend new insight designing efficient quality-of-service routing policies in large networks.

Keywords: QoS, Link-State Routing, Dijkstra, Path Selection, Path Computation.

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2370 Stability Criteria for Uncertainty Markovian Jumping Parameters of BAM Neural Networks with Leakage and Discrete Delays

Authors: Qingqing Wang, Baocheng Chen, Shouming Zhong

Abstract:

In this paper, the problem of stability criteria for Markovian jumping BAM neural networks with leakage and discrete delays has been investigated. Some new sufficient condition are derived based on a novel Lyapunov-Krasovskii functional approach. These new criteria based on delay partitioning idea are proved to be less conservative because free-weighting matrices method and a convex optimization approach are considered. Finally, one numerical example is given to illustrate the the usefulness and feasibility of the proposed main results.

Keywords: Stability, Markovian jumping neural networks, Timevarying delays, Linear matrix inequality.

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2369 Exponential State Estimation for Neural Networks with Leakage, Discrete and Distributed Delays

Authors: Liyuan Wang, Shouming Zhong

Abstract:

In this paper, the design problem of state estimator for neural networks with the mixed time-varying delays are investigated by constructing appropriate Lyapunov-Krasovskii functionals and using some effective mathematical techniques. In order to derive several conditions to guarantee the estimation error systems to be globally exponential stable, we transform the considered systems into the neural-type time-delay systems. Then with a set of linear inequalities(LMIs), we can obtain the stable criteria. Finally, three numerical examples are given to show the effectiveness and less conservatism of the proposed criterion.

Keywords: State estimator, Neural networks, Globally exponential stability.

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2368 Artificial Neural Networks for Identification and Control of a Lab-Scale Distillation Column Using LABVIEW

Authors: J. Fernandez de Canete, S. Gonzalez-Perez, P. del Saz-Orozco

Abstract:

LABVIEW is a graphical programming language that has its roots in automation control and data acquisition. In this paper we have utilized this platform to provide a powerful toolset for process identification and control of nonlinear systems based on artificial neural networks (ANN). This tool has been applied to the monitoring and control of a lab-scale distillation column DELTALAB DC-SP. The proposed control scheme offers high speed of response for changes in set points and null stationary error for dual composition control and shows robustness in presence of externally imposed disturbance.

Keywords: Distillation, neural networks, LABVIEW, monitoring, identification, control.

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2367 Using Electrical Impedance Tomography to Control a Robot

Authors: Shayan Rezvanigilkolaei, Shayesteh Vefaghnematollahi

Abstract:

Electrical impedance tomography is a non-invasive medical imaging technique suitable for medical applications. This paper describes an electrical impedance tomography device with the ability to navigate a robotic arm to manipulate a target object. The design of the device includes various hardware and software sections to perform medical imaging and control the robotic arm. In its hardware section an image is formed by 16 electrodes which are located around a container. This image is used to navigate a 3DOF robotic arm to reach the exact location of the target object. The data set to form the impedance imaging is obtained by having repeated current injections and voltage measurements between all electrode pairs. After performing the necessary calculations to obtain the impedance, information is transmitted to the computer. This data is fed and then executed in MATLAB which is interfaced with EIDORS (Electrical Impedance Tomography Reconstruction Software) to reconstruct the image based on the acquired data. In the next step, the coordinates of the center of the target object are calculated by image processing toolbox of MATLAB (IPT). Finally, these coordinates are used to calculate the angles of each joint of the robotic arm. The robotic arm moves to the desired tissue with the user command.

Keywords: Electrical impedance tomography, EIT, Surgeon robot, image processing of Electrical impedance tomography.

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2366 A New Technique for Solar Activity Forecasting Using Recurrent Elman Networks

Authors: Salvatore Marra, Francesco C. Morabito

Abstract:

In this paper we present an efficient approach for the prediction of two sunspot-related time series, namely the Yearly Sunspot Number and the IR5 Index, that are commonly used for monitoring solar activity. The method is based on exploiting partially recurrent Elman networks and it can be divided into three main steps: the first one consists in a “de-rectification" of the time series under study in order to obtain a new time series whose appearance, similar to a sum of sinusoids, can be modelled by our neural networks much better than the original dataset. After that, we normalize the derectified data so that they have zero mean and unity standard deviation and, finally, train an Elman network with only one input, a recurrent hidden layer and one output using a back-propagation algorithm with variable learning rate and momentum. The achieved results have shown the efficiency of this approach that, although very simple, can perform better than most of the existing solar activity forecasting methods.

Keywords: Elman neural networks, sunspot, solar activity, time series prediction.

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2365 Delay-Dependent Stability Analysis for Neutral Type Neural Networks with Uncertain Parameters and Time-Varying Delay

Authors: Qingqing Wang, Shouming Zhong

Abstract:

In this paper, delay-dependent stability analysis for neutral type neural networks with uncertain paramters and time-varying delay is studied. By constructing new Lyapunov-Krasovskii functional and dividing the delay interval into multiple segments, a novel sufficient condition is established to guarantee the globally asymptotically stability of the considered system. Finally, a numerical example is provided to illustrate the usefulness of the proposed main results.

Keywords: Neutral type neural networks, Time-varying delay, Stability, Linear matrix inequality(LMI).

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2364 Influence of Noise on the Inference of Dynamic Bayesian Networks from Short Time Series

Authors: Frank Emmert Streib, Matthias Dehmer, Gökhan H. Bakır, Max Mühlhauser

Abstract:

In this paper we investigate the influence of external noise on the inference of network structures. The purpose of our simulations is to gain insights in the experimental design of microarray experiments to infer, e.g., transcription regulatory networks from microarray experiments. Here external noise means, that the dynamics of the system under investigation, e.g., temporal changes of mRNA concentration, is affected by measurement errors. Additionally to external noise another problem occurs in the context of microarray experiments. Practically, it is not possible to monitor the mRNA concentration over an arbitrary long time period as demanded by the statistical methods used to learn the underlying network structure. For this reason, we use only short time series to make our simulations more biologically plausible.

Keywords: Dynamic Bayesian networks, structure learning, gene networks, Markov chain Monte Carlo, microarray data.

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2363 Stability Analysis of Impulsive BAM Fuzzy Cellular Neural Networks with Distributed Delays and Reaction-diffusion Terms

Authors: Xinhua Zhang, Kelin Li

Abstract:

In this paper, a class of impulsive BAM fuzzy cellular neural networks with distributed delays and reaction-diffusion terms is formulated and investigated. By employing the delay differential inequality and inequality technique developed by Xu et al., some sufficient conditions ensuring the existence, uniqueness and global exponential stability of equilibrium point for impulsive BAM fuzzy cellular neural networks with distributed delays and reaction-diffusion terms are obtained. In particular, the estimate of the exponential convergence rate is also provided, which depends on system parameters, diffusion effect and impulsive disturbed intention. It is believed that these results are significant and useful for the design and applications of BAM fuzzy cellular neural networks. An example is given to show the effectiveness of the results obtained here.

Keywords: Bi-directional associative memory, fuzzy cellular neuralnetworks, reaction-diffusion, delays, impulses, global exponentialstability.

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2362 Electrical Impedance Imaging Using Eddy Current

Authors: A. Ambia, T. Takemae, Y. Kosugi, M. Hongo

Abstract:

Electric impedance imaging is a method of reconstructing spatial distribution of electrical conductivity inside a subject. In this paper, a new method of electrical impedance imaging using eddy current is proposed. The eddy current distribution in the body depends on the conductivity distribution and the magnetic field pattern. By changing the position of magnetic core, a set of voltage differences is measured with a pair of electrodes. This set of voltage differences is used in image reconstruction of conductivity distribution. The least square error minimization method is used as a reconstruction algorithm. The back projection algorithm is used to get two dimensional images. Based on this principle, a measurement system is developed and some model experiments were performed with a saline filled phantom. The shape of each model in the reconstructed image is similar to the corresponding model, respectively. From the results of these experiments, it is confirmed that the proposed method is applicable in the realization of electrical imaging.

Keywords: Back projection algorithm, electrical impedancetomography, eddy current, magnetic inductance tomography.

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2361 Smart Trust Management for Vehicular Networks

Authors: Amel Ltifi, Ahmed Zouinkhi, Med Salim Bouhlel

Abstract:

Spontaneous networks such as VANET are in general deployed in an open and thus easily accessible environment. Therefore, they are vulnerable to attacks. Trust management is one of a set of security solutions dedicated to this type of networks. Moreover, the strong mobility of the nodes (in the case of VANET) makes the establishment of a trust management system complex. In this paper, we present a concept of ‘Active Vehicle’ which means an autonomous vehicle that is able to make decision about trustworthiness of alert messages transmitted about road accidents. The behavior of an “Active Vehicle” is modeled using Petri Nets.

Keywords: Component, active vehicle, cooperation, petri nets, trust management, VANET.

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2360 Finding Equilibrium in Transport Networks by Simulation and Investigation of Behaviors

Authors: Gábor Szűcs, Gyula Sallai

Abstract:

The goal of this paper is to find Wardrop equilibrium in transport networks at case of uncertainty situations, where the uncertainty comes from lack of information. We use simulation tool to find the equilibrium, which gives only approximate solution, but this is sufficient for large networks as well. In order to take the uncertainty into account we have developed an interval-based procedure for finding the paths with minimal cost using the Dempster-Shafer theory. Furthermore we have investigated the users- behaviors using game theory approach, because their path choices influence the costs of the other users- paths.

Keywords: Dempster-Shafer theory, S-O and U-Otransportation network, uncertainty of information, Wardropequilibrium.

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2359 Evaluation of Context Information for Intermittent Networks

Authors: S. Balaji, E. Golden Julie, Y. Harold Robinson

Abstract:

The context aware adaptive routing protocol is presented for unicast communication in intermittently connected mobile ad hoc networks (MANETs). The selection of the node is done by the Kalman filter prediction theory and it also makes use of utility functions. The context aware adaptive routing is defined by spray and wait technique, but the time consumption in delivering the message is too high and also the resource wastage is more. In this paper, we describe the spray and focus routing scheme for avoiding the existing problems.

Keywords: Context aware adaptive routing, Kalman filter prediction, spray and wait, spray and focus, intermittent networks.

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2358 Analysis of Data Gathering Schemes for Layered Sensor Networks with Multihop Polling

Authors: Bhed Bahadur Bista, Danda B. Rawat

Abstract:

In this paper, we investigate multihop polling and data gathering schemes in layered sensor networks in order to extend the life time of the networks. A network consists of three layers. The lowest layer contains sensors. The middle layer contains so called super nodes with higher computational power, energy supply and longer transmission range than sensor nodes. The top layer contains a sink node. A node in each layer controls a number of nodes in lower layer by polling mechanism to gather data. We will present four types of data gathering schemes: intermediate nodes do not queue data packet, queue single packet, queue multiple packets and aggregate data, to see which data gathering scheme is more energy efficient for multihop polling in layered sensor networks.

Keywords: layered sensor network, polling, data gatheringschemes.

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2357 Existence and Stability Analysis of Discrete-time Fuzzy BAM Neural Networks with Delays and Impulses

Authors: Chao Wang, Yongkun Li

Abstract:

In this paper, the discrete-time fuzzy BAM neural network with delays and impulses is studied. Sufficient conditions are obtained for the existence and global stability of a unique equilibrium of this class of fuzzy BAM neural networks with Lipschitzian activation functions without assuming their boundedness, monotonicity or differentiability and subjected to impulsive state displacements at fixed instants of time. Some numerical examples are given to demonstrate the effectiveness of the obtained results.

Keywords: Discrete-time fuzzy BAM neural networks, ımpulses, global exponential stability, global asymptotical stability, equilibrium point.

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2356 Analysis of Dropped Call Rate for Long Term Evolution Networks in Bayelsa State, Nigeria

Authors: Chibuzo Emeruwa, Nnamdi N. Omehe

Abstract:

This work attempts to effectively compare Dropped Call Rate (DCR) against industry benchmarks and competitor networks to identify areas for improvement and sets performance targets. Four mobile telecommunication networks operational in Bayelsa State Nigeria were considered. Results obtained shows that MTN and Airtel performed well within the regulator’s benchmark of ≤ 1% in all cases, while Globacom and 9moblie had instances where their performance fell outside the benchmark. The maximum values obtained within the period in view was 18.52% and it was in March 2016 for Globacom while the least value recorded is 0.00% and it was in September 2018 for 9mobile. In the seven years period under review, MTN and Airtel performed within the Nigerian Communication Commission’s (NCC) benchmark all through. This affirms that it is possible for the networks to perform optimally if adequate measures are put in place for improved Quality of Service (QoS).

Keywords: Attempted calls, data, dropped call rate, handover failure rate, key performance indicator, mobile network.

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2355 Mapping Semantic Networks to Undirected Networks

Authors: Marko A. Rodriguez

Abstract:

There exists an injective, information-preserving function that maps a semantic network (i.e a directed labeled network) to a directed network (i.e. a directed unlabeled network). The edge label in the semantic network is represented as a topological feature of the directed network. Also, there exists an injective function that maps a directed network to an undirected network (i.e. an undirected unlabeled network). The edge directionality in the directed network is represented as a topological feature of the undirected network. Through function composition, there exists an injective function that maps a semantic network to an undirected network. Thus, aside from space constraints, the semantic network construct does not have any modeling functionality that is not possible with either a directed or undirected network representation. Two proofs of this idea will be presented. The first is a proof of the aforementioned function composition concept. The second is a simpler proof involving an undirected binary encoding of a semantic network.

Keywords: general-modeling, multi-relational networks, semantic networks

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2354 Tabu Search Approach to Solve Routing Issues in Communication Networks

Authors: Anant Oonsivilai, Wichai Srisuruk, Boonruang Marungsri, Thanatchai Kulworawanichpong

Abstract:

Optimal routing in communication networks is a major issue to be solved. In this paper, the application of Tabu Search (TS) in the optimum routing problem where the aim is to minimize the computational time and improvement of quality of the solution in the communication have been addressed. The goal is to minimize the average delays in the communication. The effectiveness of Tabu Search method is shown by the results of simulation to solve the shortest path problem. Through this approach computational cost can be reduced.

Keywords: Communication networks, optimum routing network, tabu search algorithm, shortest path.

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