Neural networks for shortest path computation and routing in computer networks

Neural Network Optimal Routing Algorithm Based on Genetic Ant Colony in IPv6 Environment

The traditional IPv6 routing algorithm has problems such as network congestion, excessive energy consumption of nodes, and shortening the life cycle of the network. In response to this phenomenon, we proposed a routing optimization algorithm based on genetic ant colony in IPv6 environment. The algorithm analyzes and studies the genetic algorithm and the ant colony algorithm systematically. We use neural network to build the initial model and combine the constraints of QoS routing. We effectively integrate the genetic algorithm and ant colony algorithm that maximize their respective advantages and apply them to the IPv6 network. At the same time, in order to avoid the accumulation of a lot of pheromones by the ant colony algorithm in the later stage of the network, we have introduced an anticongestion reward and punishment mechanism. By comparing the search path with the optimal path, rewards and punishments are based on whether the network path is smooth or not. Finally, it is judged whether the result meets the condition, and the optimal solution obtained is passed to the BP neural network for training; otherwise, iterative iterations are required until the optimal solution is satisfied. The experimental results show that the algorithm can effectively adapt to the IPv6 routing requirements and can effectively solve the user's needs for network service quality, network performance, and other aspects.

Research on Application of Artificial Intelligence in Computer Network Technology

With the continuous expansion of the application scope of computer network technology, various malicious attacks that exist in the Internet range have caused serious harm to computer users and network resources. This paper attempts to apply artificial intelligence (AI) to computer network technology and research on the application of AI in computing network technology. Designing an intrusion detection model based on improved back propagation (BP) neural network. By studying the attack principle, analyzing the characteristics of the attack method, extracting feature data, establishing feature sets, and using the agent technology as the supporting technology, the simulation experiment is used to prove the improvement effect of the system in terms of false alarm rate, convergence speed, and false negative rate, the rate reached 86.7%. The results show that this fast algorithm reduces the training time of the network, reduces the network size, improves the classification performance, and improves the intrusion detection rate.

A time-delay neural network for solving time-dependent shortest path problem

This paper concerns the time-dependent shortest path problem, which is difficult to come up with global optimal solution by means of classical shortest path approaches such as Dijkstra, and pulse-coupled neural network (PCNN). In this study, we propose a time-delay neural network (TDNN) framework that comes with the globally optimal solution when solving the time-dependent shortest path problem. The underlying idea of TDNN comes from the following mechanism: the shortest path depends on the earliest auto-wave (from start node) that arrives at the destination node. In the design of TDNN, each node on a network is considered as a neuron, which comes in the form of two units: time-window unit and auto-wave unit. Time-window unit is used to generate auto-wave in each time window, while auto-wave unit is exploited here to update the state of auto-wave. Whether or not an auto-wave leaves a node (neuron) depends on the state of auto-wave. The evaluation of the performance of the proposed approach was carried out based on online public Cordeau instances and New York Road instances. The proposed TDNN was also compared with the quality of classical approaches such as Dijkstra and PCNN.

A minimum resource neural network framework for solving multiconstraint shortest path problems

Characterized by using minimum hard (structural) and soft (computational) resources, a novel parameter-free minimal resource neural network (MRNN) framework is proposed for solving a wide range of single-source shortest path (SP) problems for various graph types. The problems are the k-shortest time path problems with any combination of three constraints: time, hop, and label constraints, and the graphs can be directed, undirected, or bidirected with symmetric and/or asymmetric traversal time, which can be real and time dependent. Isomorphic to the graph where the SP is to be sought, the network is activated by generating autowave at source neuron and the autowave travels automatically along the paths with the speed of a hop in an iteration. Properties of the network are studied, algorithms are presented, and computation complexity is analyzed. The framework guarantees globally optimal solutions of a series of problems during the iteration process of the network, which provides insight into why even the SP is still too long to be satisfied. The network facilitates very large scale integrated circuit implementation and adapt to very large scale problems due to its massively parallel processing and minimum resource utilization. When implemented in a sequentially processing computer, experiments on synthetic graphs, road maps of cities of the USA, and vehicle routing with time windows indicate that the MRNN is especially efficient for large scale sparse graphs and even dense graphs with some constraints, e.g., the CPU time taken and the iteration number used for the road maps of cities of the USA is even less than  ∼ 2% and 0.5% that of the Dijkstra's algorithm.

A neural network for shortest path computation

This paper presents a new neural network to solve the shortest path problem for inter-network routing. The proposed solution extends the traditional single-layer recurrent Hopfield architecture introducing a two-layer architecture that automatically guarantees an entire set of constraints held by any valid solution to the shortest path problem. This new method addresses some of the limitations of previous solutions, in particular the lack of reliability in what concerns successful and valid convergence. Experimental results show that an improvement in successful convergence can be achieved in certain classes of graphs. Additionally, computation performance is also improved at the expense of slightly worse results.

A neural networks-based hybrid routing protocol for wireless mesh networks

The networking infrastructure of wireless mesh networks (WMNs) is decentralized and relatively simple, but they can display reliable functioning performance while having good redundancy. WMNs provide Internet access for fixed and mobile wireless devices. Both in urban and rural areas they provide users with high-bandwidth networks over a specific coverage area. The main problems affecting these networks are changes in network topology and link quality. In order to provide regular functioning, the routing protocol has the main influence in WMN implementations. In this paper we suggest a new routing protocol for WMN, based on good results of a proactive and reactive routing protocol, and for that reason it can be classified as a hybrid routing protocol. The proposed solution should avoid flooding and creating the new routing metric. We suggest the use of artificial logic—i.e., neural networks (NNs). This protocol is based on mobile agent technologies controlled by a Hopfield neural network. In addition to this, our new routing metric is based on multicriteria optimization in order to minimize delay and blocking probability (rejected packets or their retransmission). The routing protocol observes real network parameters and real network environments. As a result of artificial logic intelligence, the proposed routing protocol should maximize usage of network resources and optimize network performance.

A Novel Constructive-Optimizer Neural Network for the Traveling Salesman Problem

In this paper, a novel constructive-optimizer neural network (CONN) is proposed for the traveling salesman problem (TSP). CONN uses a feedback structure similar to Hopfield-type neural networks and a competitive training algorithm similar to the Kohonen-type self-organizing maps (K-SOMs). Consequently, CONN is composed of a constructive part, which grows the tour and an optimizer part to optimize it. In the training algorithm, an initial tour is created first and introduced to CONN. Then, it is trained in the constructive phase for adding a number of cities to the tour. Next, the training algorithm switches to the optimizer phase for optimizing the current tour by displacing the tour cities. After convergence in this phase, the training algorithm switches to the constructive phase anew and is continued until all cities are added to the tour. Furthermore, we investigate a relationship between the number of TSP cities and the number of cities to be added in each constructive phase. CONN was tested on nine sets of benchmark TSPs from TSPLIB to demonstrate its performance and efficiency. It performed better than several typical Neural networks (NNs), including KNIES_TSP_Local, KNIES_TSP_Global, Budinich's SOM, Co-Adaptive Net, and multivalued Hopfield network as wall as computationally comparable variants of the simulated annealing algorithm, in terms of both CPU time and accuracy. Furthermore, CONN converged considerably faster than expanding SOM and evolved integrated SOM and generated shorter tours compared to KNIES_DECOMPOSE. Although CONN is not yet comparable in terms of accuracy with some sophisticated computationally intensive algorithms, it converges significantly faster than they do. Generally speaking, CONN provides the best compromise between CPU time and accuracy among currently reported NNs for TSP.

Neural network based optimal routing algorithm for communication networks

This paper presents the capability of the neural networks as a computational tool for solving constrained optimization problem, arising in routing algorithms for the present day communication networks. The application of neural networks in the optimum routing problem, in case of packet switched computer networks, where the goal is to minimize the average delays in the communication have been addressed. The effectiveness of neural network is shown by the results of simulation of a neural design to solve the shortest path problem. Simulation model of neural network is shown to be utilized in an optimum routing algorithm known asflow deviation algorithm. It is also shown that the model will enable the routing algorithm to be implemented in real time and also to be adaptive to changes in link costs and network topology.

Solving linear integer programming problems by a novel neural model

The paper deals with integer linear programming problems. As is well known, these are extremely complex problems, even when the number of integer variables is quite low. Literature provides examples of various methods to solve such problems, some of which are of a heuristic nature. This paper proposes an alternative strategy based on the Hopfield neural network. The advantage of the strategy essentially lies in the fact that hardware implementation of the neural model allows for the time required to obtain a solution so as not depend on the size of the problem to be solved. The paper presents a particular class of integer linear programming problems, including well-known problems such as the Travelling Salesman Problem and the Set Covering Problem. After a brief description of this class of problems, it is demonstrated that the original Hopfield model is incapable of supplying valid solutions. This is attributed to the presence of constant bias currents in the dynamic of the neural model. A demonstration of this is given and then a novel neural model is presented which continues to be based on the same architecture as the Hopfield model, but introduces modifications thanks to which the integer linear programming problems presented can be solved. Some numerical examples and concluding remarks highlight the solving capacity of the novel neural model.

Solving constraint satisfaction and optimization problems by a neuro-fuzzy approach

The solution of constrained satisfaction and constrained optimization problems using a Hopfield model requires determination of the values of a certain number of coefficients linked to the surrounding conditions of the problem. It is quite difficult to determine these values, mainly because a heuristic search is necessary. This is not only time-consuming but may lead to solutions that are far from optimal, or even nonvalid ones. So far, there have been no works in literature offering a general method for the search for coefficents with will guarantee optimal or close to optimal solutions. This paper proposes a fuzzy approach which allows automatic determination of Hopfield coefficients.

The neural network approach to a parallel decentralized network routing

With the progress of high-speed optical transmission and packet switching, a large capacity packet-based multi-media communication network is expected to spread rapidly. One of the key issues in these networks is the network routing that chooses the route to the destination for packet transmission in the network. In most previous work, the whole network is mapped to a large size-Hopfield-type neural network. Hence, the network routing by this method is not beyond the centralized control. In this paper, a parallel decentralized Network Routing method is presented. The model comprises an interconnection of groups of an intraconnected network, which is fully connected, and resides at each node of the communication network. Since the dynamics of each neuron in the whole system follows a unique state equation, we can see easily how the update of a neuron maps to real world network routing problems. Most important, becauase of the dynamics of the neurons with such a high speed of convergence, the model has the ability to achieve a sub-optimum routing solution in a real-time application. Finally, simulation results validate the proposed method.

Primal and dual assignment networks

This paper presents two recurrent neural networks for solving the assignment problem. Simplifying the architecture of a recurrent neural network based on the primal assignment problem, the first recurrent neural network, called the primal assignment network, has less complex connectivity than its predecessor. The second recurrent neural network, called the dual assignment network, based on the dual assignment problem, is even simpler in architecture than the primal assignment network. The primal and dual assignment networks are guaranteed to make optimal assignment. The applications of the primal and dual assignment networks for sorting and shortest-path routing are discussed. The performance and operating characteristics of the dual assignment network are demonstrated by means of illustrative examples.

Neural networks for process scheduling in real-time communication systems

This paper presents the use of Hopfield-type neural networks for process scheduling in the area of factory automation, where bus-based communication systems, called FieldBuses, are widely used to connect sensors and actuators to the control systems. We show how it overcomes the problem of the computational complexity of the algorithmic solution. The neural model proposed allows several processes to be scheduled simultaneously; the time required is polynomial with respect to the number of processes being scheduled. This feature allows real-time process scheduling and makes it possible for the scheduling table to adapt to changes in process control features. The paper presents the neural model for process scheduling and assesses its computational complexity, pointing out the drastic reduction in the time needed to generate a schedule as compared with the algorithmic scheduling solution. Finally, the authors propose an on-line scheduling strategy based on the neural model which can achieve real-time adaptation of the scheduling table to changes in the manufacturing environment.