Optimal Routing Algorithm Research Articles

Dynamic Routing Optimization Algorithm for Software Defined Networking

Time and space complexity is the most critical problem of the current routing optimization algorithms for Software Defined Networking (SDN). To overcome this complexity, researchers use meta-heuristic techniques inside the routing optimization algorithms in the OpenFlow (OF) based large scale SDNs. This paper proposes a hybrid meta-heuristic algorithm to optimize the dynamic routing problem for the large scale SDNs. Due to the dynamic nature of SDNs, the proposed algorithm uses a mutation operator to overcome the memory-based problem of the ant colony algorithm. Besides, it uses the box-covering method and the k-means clustering method to divide the SDN network to overcome the problem of time and space complexity. The results of the proposed algorithm compared with the results of other similar algorithms and it shows that the proposed algorithm can handle the dynamic network changing, reduce the network congestion, the delay and running times and the packet loss rates.

NSGA-II Based Joint Topology and Routing Optimization of Flying Backhaul Networks

This paper addresses the problem of optimizing the deployment of Flying Backhaul Networks (FBNs). The latter comprise Unmanned Aerial Vehicles (UAVs), which are used as access points to provide coverage to a set of ground nodes deployed in a target area. The optimization problem is addressed by means of a Multi-Objective Optimization Algorithm (MOEA), which calculates Pareto curves of UAV placement, providing different trade-offs between the considered objectives: (1) to minimize the number of UAVs, and (2) to maximize the Packet Delivery Ratio (PDR). The selected MOEA is NSGA-II. An embedded single objective Genetic Algorithm (inner-GA) is used to optimize routing, finding the paths that maximize the PDR. In order to obtain consistent solutions for the PDR taking into account MAC layer contention, the scheme makes use of an existing fixed-point algorithm (FPA). Simulation results were obtained for different scenarios combining average versus maximin PDR objective funtions, two different routing optimization algorithms, as well as single sink versus multiple sink traffic patterns.

A Two-Stage Genetic Algorithm for Battery Sizing and Route Optimization of Medium-Duty Electric Delivery Fleets

For delivery fleets with medium-duty electric trucks, battery sizing and energy efficient routing play an important role in cost reduction. In scenarios where periodic trends such as weekly or monthly delivery demands exist, the optimization problem needs to consider the requests for each day in the time horizon simultaneously, which is a challenging task to address. The existing heuristic and metaheuristic approaches designed for a single instance of vehicle routing problem (VRP) may not guarantee an optimal solution over the longer period of interest. In this paper, a two-stage grouping genetic algorithm (GGA) that builds on the existing GA approach for delivery cost minimization is proposed. Independent VRPs are solved using a GGA with battery degradation in consideration. The resulting population is reformulated in a way such that a second GGA can utilize the information to converge the solution to a single battery combination and the respective daily optimal routes that minimize the operational cost over a fixed time horizon. Compared to the approaches using single-day data, the two-stage GGA was able to obtain solutions that give up to 7.31% lower daily cost of operation.

Optimization of routing and wavelength optimization algorithm for optical transport network based on reinforcement learning

Optimization of routing and wavelength optimization algorithm for optical transport network based on reinforcement learning

Energy Efficiency Optimization Routing Algorithm for Wireless Ultraviolet Cooperative UAV swarm

Energy Efficiency Optimization Routing Algorithm for Wireless Ultraviolet Cooperative UAV swarm

QoS Routing Algorithm for OBS Networks Based on a Multi-Objective Genetic Algorithm

To optimize the QoS of optical burst switching networks, a QoS routing optimization algorithm based on a multi-objective genetic algorithm is proposed. A Bayesian network model is used to locate the fault of optical burst switching network and obtain the fault location of the transmission link of optical burst switching network; In this position, the routing optimization algorithm based on a multi-objective genetic algorithm transforms the multi constrained network quality of service routing optimization problem into a constrained multi-objective routing optimization problem. Under multiple constraints, the best path of optical burst switching network service is obtained to realize the optical burst switching network quality of service routing optimization. The results show that after applying the proposed algorithm, the average delay of video, text and picture transmission in an optical burst switching network is less than 400ms. The proposed algorithm can improve the packet delivery rate of information transmission in an optical burst switching network, reduce the transmission delay, blocking probability and use cost of an optical burst switching network, and optimize the service quality of an optical burst switching network.

Data-Driven Bus Route Optimization Algorithm Under Sudden Interruption of Public Transport

With the continuous expansion of urban traffic operation scale, public transport emergencies occur from time to time, causing serious traffic jams and potential safety hazards in a short time. In view of this, a new adaptive bus route optimization strategy based on the emergency demand responsive public transport is proposed. Firstly, in order to improve the fine-grained passenger carrying capacity of emergency demand responsive public transport and build a clustering model of passenger information, this paper proposes an adaptive clustering algorithm, which considers the main influencing factors such as vehicle capacity, passenger travel time window and the number of stations visited. Aiming at minimizing the cost of vehicle operation and passenger traffic, a multi-objective optimization model of emergency bus route is constructed based on Vehicle Routing Problems with Time Windows (VRPTW) to ensure the operation efficiency of emergency bus. Secondly, a Modified Adaptive Large Neighborhood Search with Nearest Vehicle Dispatch (NVD) algorithm (MALNSN) is proposed, which is an extension of the Adaptive Large Neighborhood Search algorithm (ALNS), by improving the generation rules of initial solutions with NVD and operator selection strategy with Modified Choice Function (MCF), and the effectiveness of algorithm is analyzed according to the Solomon benchmark. The average gain of the proposed MALNSN algorithm is 17.11% higher than that of the original algorithm. Finally, based on the actual road network, experiments are carried out to compare the proposed algorithm with the representative algorithms. The experimental results show that the MALNSN algorithm proposed in this paper can not only ensure the stability of the algorithm, but also formulate a reasonable route optimization strategy in a shorter time, effectively reducing the consumption of transport capacity resources, improving the operation efficiency of public transport and increasing the accessibility of public transport. The theoretical analysis was consistent with the experimental results.

PEDTARA: Priority-Based Energy Efficient, Delay and Temperature Aware Routing Algorithm Using Multi-Objective Genetic Chaotic Spider Monkey Optimization for Critical Data Transmission in WBANs

Software-Defined Wireless Body Area Network (WBAN)s have gained significance in emergency healthcare applications for remote patients. Prioritization of healthcare data traffic has a high influence on the congestion and delay in the WBAN routing process. Currently, the energy constraints, packet loss, retransmission delay and increased sensor heat are pivotal research challenges in WBAN. These challenges also degrade the network lifetime and create serious issues for critical health data transmission. In this context, a Priority-based Energy-efficient, Delay and Temperature Aware Routing Algorithm (PEDTARA) is presented in this paper using a hybrid optimization algorithm of Multi-objective Genetic Chaotic Spider Monkey Optimization (MGCSMO). This proposed optimized routing algorithm is designed by incorporating the benefits of chaotic and genetic operators to the position updating function of enhanced Spider Monkey Optimization. For the prioritized routing process, initially, the patient data transmission in the WBAN is categorized into normal, on-demand and emergency data transmissions. Each category is ensured with efficient routing using the three different strategies of the suggested PEDTARA. PEDTARA performs optimal shortest path routing for normal data, energy-efficient emergency routing for high priority critical data and faster but priority verified routing for on-demand data. Thus, the proposed PEDTARA ensures energy-efficient, congestion-controlled and delay and temperature aware routing at any given period of health monitoring. Experiments were performed over a high-performance simulation scenario and the evaluation results showed that the proposed PEDTARA performs efficient routing better than the traditional approaches in terms of energy, temperature, delay, congestion and network lifetime.

Researching, building the routing algorithm in Zigbee networks

Zigbee wireless network built on IEEE 802.15.4 standard is becoming one of the most popular wireless networks in modern IoT devices. One of the disadvantages of Zigbee networks is the short transmission distance between devices. This paper focuses on researching and comparing routing algorithms in Zigbee networks, thereby building the optimal routing algorithm in the existing system. The paper’s objective is to form the basis for making Zigbee tree and mesh networks, which improves the transmission distance for Zigbee networks better than the star network.

OPTIMIZATION OF DISTRIBUTION ROUTE USING DIJKSTRA‟S BASED GREEDY ALGORITHM: CASE OF RETAIL CHAIN

In cities, the efficiency of resolving pickup and delivery routes is dependent on calculating the shortest routes among shops in a complicated road network directly. Selecting the best path is a practical problem with a high frequency of application in everyday life, and it has practical implications for the research. One of the most well-known optimal path selection methods is the Dijkstra's based greedy Algorithm. In actuality, there exist numerous models for route selection; however, the main focus of this study is on the implementation of one of them, shortest path algorithms for distribution routing optimization. When solving the optimal path problem, it also serves as the theoretical foundation for many empirical models. Many factors will affect your decision on the best path to choose. In practise, the selection conditions will frequently contain many precedence conditions and will entirely pick the influence path. The optimization algorithm is implemented and confirmed in this paper through analysis and actual application.

TSOR: Thompson Sampling-Based Opportunistic Routing

Routing is a fundamental problem and has been extensively studied in various networks. However, in highly dynamic networks (e.g., wireless ad hoc networks), nodes have limited transmission opportunities due to high mobility, noise and interference, where traditional routing is often not the best approach. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Opportunistic routing (OR)</i> , on the other hand, can effectively minimize the routing cost (e.g., the number of hops) and improve the success of routing by utilizing link metrics. However, the link metrics are usually unknown in advance and changing. In this paper, we design an adaptive algorithm called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Thompson sampling-based opportunistic routing (TSOR)</i> motivated by the distributed Bellman-Ford algorithms. TSOR is able to learn the link metrics and route packets simultaneously to reduce the overall cost. Theoretically, we show a lower bound and an upper bound of the cumulative regret (i.e., performance gap) between TSOR and the optimal routing algorithm that knows all link metrics in advance. The regret increases sublinearly with respect to the number of packets, and has a lower order in terms of the network size than the best-known results. Furthermore, we compare TSOR with the state-of-the-art algorithms, and the evaluation results show that TSOR has a lower regret and a faster convergence rate to the optimal policy than the state-of-the-art algorithms.

Research on Vehicle Scheduling of Cross-Regional Collection Using Hierarchical Agglomerative Clustering and Algorithm Optimization

At present, municipal solid waste (MSW) collection is based on the divide-regional operation mode, which has many deficiencies. This paper proposes a cross-regional operation scheme. Through the initial assignment, type labeling, and reassignment, and use the improved hierarchical agglomerative clustering (IHAC) algorithm and garbage collecting route optimization (GCRO) algorithm to realize intelligent allocation of garbage and scheduling route planning of collection vehicles. The experimental results demonstrate the proposed scheme improves the utilization of vehicle resources, reduces the operating cost, realizes the balanced allocation of garbage, and solves the problems caused by limitations of the original operation scheme, which demonstrates the feasibility and effectiveness of the cross-regional operation.

Efficiency-complexity evaluation methods of routing algorithms in mobile ad hoc networks

Routing in multidomain and multilayer networks is the subject of constant theoretical research, with special emphasis on routing optimization algorithms based on several criteria. Such research results in new proposals. The basic task of the algorithm is to perform the given task in a finite and reasonable period of time and with reasonable resource requirements. When new solutions are compared with previous solutions, it is necessary to consider as much information as possible about the characteristics and differences between these algorithms, which ultimately determines the degree of success of the algorithm. Routing algorithms depend on the goals to be achieved and most often solve a certain group of problems with certain simplifications of the overall problem and to the detriment of performance that are not crucial for a given routing optimization problem. Therefore, it is necessary to have acceptable methods for efficiency-complexity evaluation methods of routing algorithms with certain, universally applicable, metrics. Several theoretical approaches, including graph theory, optimization theory, complexity theory, allow approaches to compare the algorithms and the results achieved with the help of these algorithms.

Incorporate ACO routing algorithm and mobile sink in wireless sensor networks

Today, science and technology is developing, particularly the internet of things (IoT), there is an increasing demand in the sensor field to serve the requirements of individuals within modern life. Wireless sensor networks (WSNs) was created to assist us to modernize our lives, saving labor, avoid dangers, and that bring high efficiency at work. There are many various routing protocols accustomed to increase the ability efficiency and network lifetime. However, network systems with one settled sink frequently endure from a hot spots issue since hubs close sinks take a lot of vitality to forward information amid the transmission method. In this paper, the authors proposed combining the colony optimization algorithm ant colony optimization (ACO) routing algorithm and mobile sink to deal with that drawback and extend the network life. The simulation results on MATLAB show that the proposed protocol has far better performance than studies within the same field.

Tonal harmony and the topology of dynamical score networks

I introduce the concept of dynamical score networks for the representation and analysis of tonal compositions: a score is interpreted as a dynamical network where every chord is a node and each progression links successive chords. This network can be viewed as a time series of a non-stationary signal, and as such, it can be partitioned for the automatic identification of tonal regions using time series analysis and change point detection without relying on comparisons with pre-determined reference sets or extensive corpora. I demonstrate that the essential features of tonal harmony, centricity, referentiality, directedness and hierarchy, emerge naturally from the network topology and its scale-free properties. Finally, solving for the minimal length path through a route optimization algorithm on these graphs provides an abstraction of harmonic sequences that can be generalized for the conception of generative models of tonal compositional design.

Semi-decentralized energy routing algorithm for minimum-loss transmission in community energy internet

Modern power grid is becoming increasingly decentralized due to the rapid penetration of distributed energy sources. The concept of Energy Internet has been proposed as an open framework for interconnecting all distributed electrical devices via energy routers. However, efficient power transmission across the complex Energy Internet depends critically on the implementation of efficient energy routing algorithm. In this paper, an optimal energy routing algorithm is proposed to minimize transmission loss and achieve efficient energy routing. A semi-decentralized algorithm is proposed to distribute the computational load to individual energy routers for relieving the computational burden of the network control center. Routing information is shared among all energy routers via a common information sharing platform. With the proposed energy routing algorithm, transmission loss is further reduced and line congestion problem can be avoided by adopting multi-path transmissions. In the presence of multiple source-sink pairs and path overlapping, congestion and conflicts are resolved by cooperative routing. Transmission paths of the coalition formed are re-planned with the objective to minimize the total transmission loss of the coalition, and transmission loss savings are fairly assigned according to the Shapley values of members. Finally, the proposed energy routing algorithm is rigorously verified by numerical simulations.

Performance Analysis of Optad-NoC: A Novel Optimized Routing Algorithm and Intelligent Router for 3D Network-on-Chip

Performance Analysis of Optad-NoC: A Novel Optimized Routing Algorithm and Intelligent Router for 3D Network-on-Chip

Hybrid Seagull Optimization Algorithm and Thermal Exchange Optimization for optimal routing in VANET

Hybrid Seagull Optimization Algorithm and Thermal Exchange Optimization for optimal routing in VANET

A multi-constraint optimal routing algorithm in LEO satellite networks

A multi-constraint optimal routing algorithm in LEO satellite networks

Optimized routing algorithm for maximizing transmission rate in D2D network

&lt;span style="font-size: 9pt; font-family: 'Times New Roman', serif;"&gt;Wireless devices have been equiping extensive services over recent years. Since most of these devices are randomly distributed, a fundamental trade-off to be addressed is the transmission rate, latency, and packet loss of the ad hoc route selection in device to device (D2D) networks. Therefore, this paper introduces a notion of weighted transmission rate and total delay, as well as the probability of packet loss. By designing optimal transmission algorithms, this proposed algorithm aims to select the best path for device-to-device communication that maximizes the transmission rate while maintaining minimum delay and packet loss. Using the Lagrange optimization method, the lagrangian optimization of rate, delay, and the probability of packet loss algorithm (LORDP) is modeled. For practical designation, we consider the fading effect of the wireless channels scenario. The proposed optimal algorithm is modeled to compute the optimal cost objective function and represents the best possible solution for the corresponding path. Moreover, a simulation for the optimized algorithm is presented based on optimal cost objective function. Simulation results establish the efficiency of the proposed LORDP algorithm&lt;/span&gt;&lt;span&gt;.&lt;/span&gt;&lt;span style="font-size: 9pt; font-family: 'Times New Roman', serif;"&gt;Wireless devices have been equiping extensive services over recent years. Since most of these devices are randomly distributed, a fundamental trade-off to be addressed is the transmission rate, latency, and packet loss of the ad hoc route selection in device to device (D2D) networks. Therefore, this paper introduces a notion of weighted transmission rate and total delay, as well as the probability of packet loss. By designing optimal transmission algorithms, this proposed algorithm aims to select the best path for device-to-device communication that maximizes the transmission rate while maintaining minimum delay and packet loss. Using the Lagrange optimization method, the lagrangian optimization of rate, delay, and the probability of packet loss algorithm (LORDP) is modeled. For practical designation, we consider the fading effect of the wireless channels scenario. The proposed optimal algorithm is modeled to compute the optimal cost objective function and represents the best possible solution for the corresponding path. Moreover, a simulation for the optimized algorithm is presented based on optimal cost objective function. Simulation results establish the efficiency of the proposed LORDP algorithm&lt;/span&gt;