In this paper, routing in computer networks is performed using a fuzzy logic strategy. The present network traffic is evenly distributed during the routing. The fuzzy based system will periodically receive the information from the network and based on this information will divert the load to other parts of the network. Simulation results are used to show the performance of the proposed method and comparison results are also provided.

Distance-based clustering methods categorize samples by optimizing a global criterion, finding ellipsoid clusters with roughly equal sizes. In contrast, density-based clustering techniques form clusters with arbitrary shapes and sizes by optimizing a local criterion. Most of these methods have several hyper-parameters, and their performance is highly dependent on the hyper-parameter setup. Recently, a Gaussian Density Distance (GDD) approach was proposed to optimize local criteria in terms of distance and density properties of samples. GDD can find clusters with different shapes and sizes without any free parameters. However, it may fail to discover the appropriate clusters due to the interfering of clustered samples in estimating the density and distance properties of remaining unclustered samples. Here, we introduce Adaptive GDD (AGDD), which eliminates the inappropriate effect of clustered samples by Adaptively updating the parameters during clustering. It is stable and can identify clusters with various shapes, sizes, and densities without adding extra parameters. The distance metrics calculating the dissimilarity between samples can affect the clustering performance. The effect of different distance measurements is also analyzed on the method. The experimental results conducted on several well-known datasets show the effectiveness of the proposed AGDD method compared to the other well-known clustering methods.

As the number of elements in large-scale massively parallel computers, Multiprocessors System-on-Chip (MP-SoCs), and peer-to-peer communication networks increases, the probability of component failure becomes significant. Consequently, fault-tolerance turns out to be a key issue in the design of such systems. Adaptive routing algorithms have been frequently suggested as a means of improving communication performance in such systems these algorithms, unlike deterministic routing, can utilize network state information to exploit the presence of multiple paths. Before such schemes can be successfully incorporated in networks, it is necessary to have a clear understanding of the factors which affect their performance potential this paper investigates the performance of nine prominent Adaptive fault-tolerant routing algorithms in wormhole-switched 2-D tori with a routing scheme suggested by Chalasani and Boppana, as an instance of a fault-tolerant method. The suggested scheme is widely used in the literature to achieve high adaptivity and support interprocessor communications in massively parallel computers due to its ability to preserve both communication performance and fault-tolerant demands in these networks. The performance measures studied in the paper are the throughput, average message latency and average usage of virtual channels per node. Results obtained through simulation suggest two classes of presented routing schemes as high performance candidates in most faulty networks Furthermore; we propose an analytical model to assess the performance behavior of a fully Adaptive routing which has been shown to be one of the most efficient routings in the torus networks. The validity of the model is demonstrated by comparing analytical results with those obtained through simulation experiments.

In recent years, mobile ad-hoc networks have been used widely due to advances in wireless technology. These networks are formed in any environment that is needed without a fixed infrastructure or centralized management. Mobile ad-hoc networks have some characteristics and advantages such as wireless medium access, multi-hop routing, low cost development, dynamic topology and etc. In these networks the nodes formed temporarily and can move freely and each node has a limited energy that is supplied by the battery. Energy-efficient routing is one of the most important and challenging issues in these networks because of the limited energy. Therefore, most researchers seek to provide a method for energy aware routing. Soft computing methods help mobile ad-hoc networks, so that these networks would be worked more efficiently. One of these methods is using intuitionistic fuzzy logic that improves the evaluation parameters such as throughput. In this paper, an intuitionistic fuzzy logic system has been used for adjusting node willingness parameter in AODV protocol. Decision about participating in the routing of each mobile node is done by the intuitionistic fuzzy logic system with remaining energy and consumption energy of each node. In order to evaluate the proposed protocol entitled IFEE-AODV (Intuitionistic Fuzzy logic for Energy Efficient routing based AODV), we simulated IFEE-AODV by using MATLAB software and compared these results with AODV(Ad hoc On-demand Distance Vector), DFES-AODV (Dynamic Fuzzy Energy State based AODV) and SFES-AODV (Static Fuzzy Energy State based AODV) protocols. The results show that this protocol in metrics of packet delivery ratio and network lifetime has better performance than other protocols.

Wireless sensor networks consist of many fixed or mobile, non-rechargeable, low-cost, and low-consumption nodes. Energy consumption is one of the most important challenges due to the non-rechargeability or high cost of sensor nodes. Hence, it is of great importance to apply some methods to reduce the energy consumption of sensors. The use of clustering-based routing is a method that reduces the energy consumption of sensors. In the present article, the Self-Adaptive Multi-objective TLBO (SAMTLBO) algorithm is applied to select the optimal cluster headers. After this process, the sensors become the closest components to cluster headers and send the data to their cluster headers. Cluster headers receive, aggregate, and send data to the sink in multiple steps using the TLBO-TS hybrid algorithm that reduces the energy consumption of the cluster heads when sending data to the sink and, ultimately, an increase in the wireless sensor network’ s lifetime. The simulation results indicate that our proposed protocol (OCRP) show better performance by 35%, 17%, and 12% compared to ALSPR, CRPD, and COARP algorithms, respectively. Conclusion: Due to the limited energy of sensors, the use of meta-heuristic methods in clustering and routing improves network performance and increases the wireless sensor network's lifetime.

The present model structure is based on modified Muskingum method for channel network simulation for obtaining a real time hydrologic model of run-off. The field application of the model was checked using three flood events of the Khosk River Basin in Shiraz-Iran, which is based on their hydrometric station records upstream and downstream. In order to optimize the model parameters, tabu search method was used and compared with the results provided by genetic algorithm. The numerical simulator was derived in explicit finite difference scheme. The statistical results when compared to the genetic algorithm show the better performance of the current method.

The classical models of vehicle routing generally focused on minimizing total distance and travel time, however in green vehicle routing problem the main objective is minimizing total emissions and fuel consumption besides the other objects.In this paper, extension of GVRP with minimizing fuel and emission costs presented that considered pickup and delivery constraints with hard time windows. Travel time in this model is not constant and speed of vehicles would be determined in regards to customers’ time widows. In this paper, a heuristic based Adaptive large neighborhood search proposed for solving the model. Construction algorithm in this method is heuristic based algorithm with proposed criterion according to pick up and delivery constrains and time windows with assumption of variable speed. Computational results confirms efficiency of this algorithm.

The lower power consumption, larger communication bandwidth, and reduced latency are advantages of optical networks over electrical communications. However, there are challenges in these networks, such as routing and connectivity issues, which result in increased network size and wastage. As networks become more complex and larger, building on-chip networks brings problems like communication costs between components and the likelihood of unpredictable failures in communication circuits. Therefore, providing an error-tolerant routing algorithm plays a crucial role in the development of on-chip network architecture. In this article, an Adaptive fault-tolerant routing algorithm will be presented, whose main objective is to create the ability to handle a reasonable number of faults without disrupting the healthy nodes in the network. The simulation results of message delay in the proposed method show a gradient norm equal to 1.1691E-5 and μ = 1E-8 for epoch=280, demonstrating its capability to reduce delay in the network. A very slight change in message delay in evaluating the proposed method also indicates the acceptability of the proposed method. Moreover, the presence of a gradient of 1.527E-3 and μ = 1E-7 for epoch=350 in the energy consumption value indicates a reduction in energy consumption compared to conventional methods in existing references, although the proposed system may incur additional overhead compared to some previous methods.