Optimal Routing Algorithm Research Articles

Jaya clustering-based algorithm for multiobjective IoV network routing optimization

Jaya clustering-based algorithm for multiobjective IoV network routing optimization

A Neural Multi-Objective Capacitated Vehicle Routing Optimization Algorithm Based on Preference Adjustment

The vehicle routing problem (VRP) is a common problem in logistics and transportation with high application value. In the past, many methods have been proposed to solve the vehicle routing problem and achieved good results, but with the development of neural network technology, solving the VRP through neural combinatorial optimization has attracted more and more attention by researchers because of its short inference time and high parallelism. PMOCO is the most state-of-the-art multi-objective vehicle routing optimization algorithm. However, in PMOCO, preferences are often uniformly selected, which may lead to uneven Pareto sets and may reduce the quality of solutions. To solve this problem, we propose a multi-objective vehicle routing optimization algorithm based on preference adjustment, which is improved from PMOCO. We incorporate the weight adjustment method in PMOCO that is able to adapt to different approximate Pareto fronts and to find solutions with better quality. We treat the weight adjustment as a sequential decision process and train it through deep reinforcement learning. We find that our method could adaptively search for a better combination of preferences and have strong robustness. Our method is experimented on multi-objective vehicle routing problems and obtained good results (about 6% improvement compared with PMOCO with 20 preferences).

Research on Deployment Scheme and Routing Optimization Algorithm of Distribution Cable Condition Monitoring Devices

In order to achieve more effective online monitoring of distribution cables, a deployment scheme of the distribution cable condition monitoring devices based on a wireless sensor network (WSN) has been proposed. The proposed deployment scheme can improve the coverage rate and extend the lifetime of the sensor network. Moreover, the clustering method, node density, and node deployment method of the wireless sensor routing algorithm are improved, and based on isosceles triangle non-uniform deployment, a routing optimization algorithm has been proposed to achieve the balance of the energy consumption of each node in the network. Firstly, according to the energy consumption model of the network, the energy consumption of each cluster is calculated, and then by taking the minimum energy consumption of data transmission between clusters as the constraint condition, the optimal clustering distance of the network is solved. Then, according to the scale of network nodes, the density of routing nodes in each cluster is calculated, and the routing nodes in the cluster are deployed by an isosceles triangle. Finally, according to the cluster head election formula, the routing cluster head nodes in each cluster are selected, and the network data information is transmitted to the base station in a multi-hop manner through the routing cluster head nodes. The simulation results indicated that, compared with the traditional routing optimization algorithms, the proposed algorithm could keep the routing coverage at more than 200% all the time, and can effectively balance the energy consumption of nodes, improve the energy utilization efficiency of the routing nodes, and at least extend the lifetime of the network by two times. Moreover, the longer the cables, the more cost savings and the better the life cycle optimization effect of the proposed scheme. In addition, the proposed scheme can greatly reduce the economic cost of network investment, compared with using the demodulator to obtain monitoring data.

Hotspots-based patrol route optimization algorithm for smart policing

Smart policing based on the analysis of big data ensures the development and sustainability of police policy. However, it is difficult to find instances in which the results of data analysis have been applied to actual policy in the field of crime prevention. The South Korean police force recognizes the need for smart policing and is engaged in various research and field support activities. Some examples that are especially relevant for crime investigation include analyzing the connections between cases and predicting the location of the next crime in a series of crimes and the location of suspects. However, it is difficult to find examples of police policy that use big data. Therefore, this study aims to suggest a model that uses big data to respond to emergency calls efficiently. First, we extract hotspots that are predicted to be locations of criminal activity based on an analysis of the association between community environment data and crime data. Second, we create a route having the shortest travel time to the crime location by developing a route optimization algorithm. Lastly, we assess the performance of the patrol routes in reflecting real-time traffic information. If the data application model suggested in this study could be adjusted and applied to the current police patrol system, the model could be used by each police department effectively.

Optimizing Last-Mile Delivery Operations: Leveraging Predictive Analytics, Technology Integration, and Sustainable Practices

In the contemporary landscape of e-commerce and retail industries, the optimization of last-mile delivery operations emerges as a pivotal element for sustaining customer satisfaction and operational efficiency. This study delves into a comprehensive exploration of various strategies aimed at refining the last-mile delivery process, a segment known for its significant challenges, including high costs, inefficiencies, and environmental concerns. By integrating predictive analytics, this research goes beyond traditional logistics methods to forecast delivery needs and driver performance accurately, allowing for pre-emptive adjustments that enhance reliability and efficiency. Furthermore, the investigation into technology integration sheds light on how advanced software solutions, such as route optimization algorithms and real-time tracking systems, can drastically reduce delivery times and operational costs while simultaneously increasing customer satisfaction levels. Additionally, the paper emphasizes sustainable practices within last-mile delivery operations, exploring eco-friendly approaches that not only mitigate environmental impact but also potentially lower delivery costs through fuel savings and efficiency gains. Through the analysis of real-world data and illustrative case studies, this research articulates a holistic framework aimed at improving last-mile delivery. Such a framework is indispensable for companies seeking to maintain a competitive edge in the fast-paced and ever-evolving retail sector. The findings of this study underscore the importance of embracing technological advancements and sustainable practices to address the multifaceted challenges of last-mile delivery, ultimately leading to improved service levels and operational excellence.

Quantum spider prioritized routing and Cramer Shoup cryptosecured data transmission in wireless body area network

The wireless body area network (WBAN) offers healthcare applications for remote patient monitoring. In healthcare, prioritizing patient data has a high impact on the energy usage, delays, and congestion involved in routing. Energy usage, battery life, and absorption rates are current research obstacles in WBAN. The path loss ratio and residual energy were generally the focus of attention during data transfer. Despite this, compromised transaction security caused serious problems with the delivery of vital health data. The Quantum Spider Cramer Shoup Public Key Cryptosystem (QS-CSPKC), which is proposed in this study, is an optimized priority-aware (for many patients) and transaction security technique for vital sensed data in WBAN, selects the optimum energy efficient and reliable path to assure secure data transfer. The proposed QS-CSPKC method employs Dual Contention Access Periods (DCAPs) for managing regular data (i.e., normal sensed or low priority data) and life-threatening data (i.e., abnormal sensed data or high priority data). After prioritizing, the Quantum Spider Monkey Optimized Routing algorithm is used to allocate an optimal shortest route for normal low priority data (i.e., with 0 as the quantum value) and an energy-efficient emergency route for critical high priority data (i.e., with 1 as the quantum value). In addition, to prevent illegal access during transmission, security keys are generated using the Cramer Shoup public key cryptosystem. By using this model, only legitimate users have access to information for which they are authorized. The evaluation results were validated through simulations, showing that the proposed QS-CSPKC method performs efficient routing and secure data transmission better than conventional techniques in terms of throughput, packet loss, average power consumption, and energy use.

Dual core processors: Coupled queues: Transient performance evaluation

High-Performance Computing (HiPC) systems routinely employ multi/many – core processors. Specifically, dual – core processors find many applications in pervasive computing devices. Dual–core processors employ buffers for queueing the incoming jobs. Traditionally, the queues at the processors are assumed to be independent and the queueing system is analyzed in equilibrium for tractability purposes. Queues are modeled using Continuous Time Markov Chains (CTMC's) and the equilibrium performance measures are determined to analyze as well as design the queueing systems. In most interesting cases, the incoming jobs are routed to the queues using the Join the Shortest Queue (JSQ) policy. Thus, with such an adaptive routing algorithm, the two queues are evidently coupled and are not statistically independent. Hence traditional equilibrium performance evaluation doesn't provide realistic performance measures. In this research paper, the two queues associated with buffers in dual-core processors are considered to be coupled. The Coupled Queues are modeled using a Quasi – Birth – and – Death (QBD) process. Using traditional results related to QBD processes, equilibrium performance measures are determined. More interestingly, we demonstrate the tractability of the computation of transient probability distribution of a QBD process. In the research literature, transient analysis of the QBD process was shown to be tractable in the Laplace transform domain. But in this research paper, we prove that the matrix exponential eQt arising in transient analysis (where Q is the generator matrix of the QBD process) can be computed directly in the time domain rendering efficient transient analysis of QBD process. Using the transient probability mass function of queue length, estimation of transient performance measures such as expected queue length, average delay, and tail distribution can be determined. Further, optimal adaptive routing algorithms for coupled queues can be designed.

A Task-Oriented Hybrid Routing Approach based on Deep Deterministic Policy Gradient

With the development of communication and transmission technologies, the applications of Internet of Things (IoT) and telemedicine are increasingly sensitive to network latency. To meet this urgent demand, learning-based routing approaches emerge, with better performance and higher flexibility. These routing approaches can be divided into single-path and multipath, which have lower transmission delay and better load balancing respectively. However, the traffic scheduling policy is not only affected by the network states but also needs to consider the requirements for traffic tasks. We propose a Task-Oriented Hybrid Routing Approach (TOHRA) based on Deep Deterministic Policy Gradient (DDPG) in Software-Defined Networking (SDN). First, the hybrid routing optimization algorithm comprehensively considers the network states and task requirements, and outputs a task-oriented hybrid routing that combines the advantages of single-path and multipath. Moreover, we design a hop-by-hop traffic segmentation model based on DDPG to output the traffic segmentation ratio on hybrid routing to adapt to the changing transmission path. Experimental results show that TOHRA achieves the best performance of load balancing and network throughput. Especially in the case of Type C tasks in Germany topology, the average network throughput of THORA is increased by 32.86%, and the average variance of link load rate is reduced by 46.62%.

Branch and price algorithm for route optimization on customized bus service

Branch and price algorithm for route optimization on customized bus service

An intelligent routing algorithm for energy prediction of 6G-powered wireless sensor networks

With the rapid development of the Internet and computers, large-scale network applications have become a research hotspot in the communication industry, engineering and other fields. Building a new wireless sensor network and intelligent communication transmission system can promote the further upgrading and improvement of the capability industry, which is also a subject that the current network academia and communication academia are trying to explore. Based on the above background environment, this paper proposes the research on energy prediction and intelligent routing algorithm of wireless sensor networks in the development of sixth generation (6G) networks. A node coverage and scheduling algorithm based on energy prediction is proposed for the overlap and non-uniformity of regions in network transmission. Secondly, the problem of energy efficient utilization in wireless sensor networks is solved, and the clustering algorithm is used to predict the use process. The working state model of network sensor is established, and the energy parameters are added to the actual prediction calculation as influencing factors. Finally, in the intelligent routing mechanism, clustering mechanism and particle swarm optimization algorithm are used to improve the path selection, so as to improve the fault tolerance of wireless network transmission in the face of large amounts of dynamic data. The research results show that the optimization and intelligent routing algorithm based on energy prediction for wireless sensor networks can prolong the service life cycle and ensure the network transmission efficiency and coverage in a long work cycle. It can ensure the energy supply of the sensor and prolong the service time through the balanced node scheduling.

Study of Optimization of Tourists' Travel Paths by Several Algorithms

The purpose of this paper is to optimize the tourism path to make the distance shorter. The article first constructed a model for tourism route planning and then used particle swarm optimization (PSO), genetic algorithm (GA), and ant colony algorithms to solve the model separately. Finally, a simulation experiment was conducted on tourist attractions in the suburbs of Taiyuan City to compare the path optimization performance of the three algorithms. The three path optimization algorithms all converged during the process of finding the optimal path. Among them, the ant colony algorithm exhibited the fastest and most stable convergence, resulting in the smallest model fitness value. The travel route obtained through the ant colony algorithm had the shortest distance, and this algorithm required minimal time for optimization. The novelty of this article lies in the enumeration and description of various algorithms used for optimizing travel paths, as well as the comparison of three different travel route optimization algorithms through simulation experiments. Doi: 10.28991/HIJ-2023-04-02-012 Full Text: PDF

Analysis of Road Accessibility by Residents and Tourists to Public Hospitals in Mallorca (Balearic Islands, Spain)

Accessibility to public hospital services is crucial for healthcare provision, particularly in regions characterized by high tourist presence, such as the Mediterranean islands. This study aims to assess the accessibility of public hospital services in Mallorca, considering access time, the geographic distribution of the resident population, and tourist accommodations. A GIS-based analysis using optimal routing algorithms and the Gini index was conducted to examine the equity in the distribution of regional and reference hospitals across four Health Sectors: Ponent, Migjorn, Tramuntana, and Llevant. The findings reveal that accessibility to regional hospitals is generally favorable, with average access times of 18, 16, 15, and 26 min, respectively, without surpassing 60 min. Accessibility to referral hospitals is comparatively lower, with 31,499 individuals located more than 1 h away. Moreover, 56% of the population is within 10 min from the referral hospital, and only 1.6% require more than 50 min to reach the hospital. Accessibility differs among Health Sectors, with Llevant experiencing the most significant impact. The hospital distribution in Mallorca is deemed to be adequate to serve residents, tourists, and the road network. Although limited in comparisons to other studies conducted in Spain, accessibility in Mallorca is fairly similar to Andalusia, Extremadura, and Catalonia. The study underscores the necessity for health infrastructure planning in order to account for the distribution of resident and tourist populations to ensure efficient and equitable services, particularly in Mediterranean island environments with a substantial tourist presence.

A Cost-Effective Sequential Route Recommender System for Taxi Drivers

This paper develops a cost-effective sequential route recommender system to provide real-time routing recommendations for vacant taxis searching for the next passenger. We propose a prediction-and-optimization framework to recommend the searching route that maximizes the expected profit of the next successful passenger pickup based on the dynamic taxi demand-supply distribution. Specifically, this system features a deep learning-based predictor that dynamically predicts the passenger pickup probability on a road segment and a recursive searching algorithm that recommends the optimal searching route. The predictor integrates a graph convolution network (GCN) to capture the spatial distribution and a long short-term memory (LSTM) to capture the temporal dynamics of taxi demand and supply. The GCN-LSTM model can accurately predict the pickup probability on a road segment with the consideration of potential taxi oversupply. Then, the dynamic distribution of pickup probability is fed into the route optimization algorithm to recommend the optimal searching routes sequentially as route inquiries emerge in the system. The recursion tree-based route optimization algorithm can significantly reduce the computational time and provide the optimal routes within seconds for real-time implementation. Finally, extensive experiments using Beijing Taxi GPS data demonstrate the effectiveness and efficiency of the proposed recommender system. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: This work was partially supported by the Hong Kong Research Grants Council [Grants CityU 21500220, CityU 11504322] and the National Natural Science Foundation of China [Grant 72201222]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0112 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0112 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

An Insight on Optimization Techniques for Uncertain and Reliable Routing in Wireless Body Area Networks

In recent times, Wireless Body Area Networks a subsection of Wireless Sensor Network is a promising technology for future healthcare realm with cutting-edge technologies that can assist healthcare professionals like doctors, nurses and biomedical engineers. Machine Learning and Internet of Things enabled medical big data is the future of healthcare sector and Medical Technology-based industries leading to applications in other sectors such as fitness tracking for commercial purposes, Sportsperson health monitoring to track their day-to-day activities and wearable devices for critical and emergency care. This comprehensive review article addresses Wireless Body Area Network state-of-art and the dependence of Optimization Techniques and Meta-heuristic Algorithms for an efficient routing path between two sensor nodes: source node and destination node and it plays an effective role in optimizing the network parameters such as radio range, energy consumption, throughput, data aggregation, clustering and routing. Designing of energy-efficient routing for wireless body are network is such a challenging task due to uncertainty in dynamic network topology, energy constraints and limited power consumption. Optimization Techniques can help the researchers to achieve the drawbacks mentioned above and energy-efficiency of the network can be improved. In this article, we focus majorly on the efficiency of usage of optimization algorithms for Wireless Body Area Network routing mechanisms and a summary of its earlier studies during 2012-2023 epoch. Genetic Algorithm, Particle Swarm Optimization, Ant Colony Optimization, Artificial Bee Colony and Firefly Optimization algorithms were discussed on achieving local optima for better results through optimization. This article provides an insight into existing gaps and further modifications to the researchers in WBAN that can motivate them to propose new ideas for reliable solutions. Performance comparison and evaluation of different bio-inspired optimization algorithms has been discussed for further improvement in optimized routing algorithms.

An Enhanced AI-Enabled Routing Optimization Algorithm for Internet of Vehicles (IoV)

An Enhanced AI-Enabled Routing Optimization Algorithm for Internet of Vehicles (IoV)

Routing optimization algorithm based on mobile agent for wireless sensor networks

A routing model about mobile agent in sensor networks was drawn out, and an optimization problem of mobile agent static route was derived. In order to optimize the route of mobile agent, a target location algorithm in wireless sensor networks was proposed. All the sensor nodes which know target compete to get the mobile agent in order to track the location of targets. Competition rules are defined by the remaining value of the energy and the distance between the nodes and the target. Simulation results showed that this algorithm could provide less energy consumption compared to other schemes and prolong survival cycle time. It balances the network energy, reduces the amount of data transmission in the network, reduces the network energy overhead, prolongs the lifetime of the entire network, and effectively reduces the network delay.

A hybrid C-GSA optimization routing algorithm for energy-efficient wireless sensor network

A hybrid C-GSA optimization routing algorithm for energy-efficient wireless sensor network

An Optimal Route Algorithm for Automated Vehicle in Monitoring Road Infrastructure

An Optimal Route Algorithm for Automated Vehicle in Monitoring Road Infrastructure

Application of Swarm Intelligence Optimization Algorithm in Logistics Delivery Path Optimization under the Background of Big Data

Traditional logistics delivery route optimization algorithm has some problems such as long time to find the optimal route. Based on this, this paper discusses swarm intelligence optimization algorithm and logistics delivery route optimization. To solve the logistics vehicle routing problem, considering that the basic ACO (ant colony optimization) has the disadvantages of slow convergence speed and easy to fall into local optimum, this paper proposes a new hybrid population optimization algorithm and applies it to VRPTW (vehicle routing problem with time windows). In addition, the concept of crowding degree in AFSA (artificial fish swarm algorithm) is introduced into ACO. In the early stage of the optimization process, a strong crowding degree limit is set to ensure that most ants are not affected by pheromone concentration to conduct random optimization. The simulation results show that the AC (accuracy rate) of this algorithm is 95.08%, which is higher than the traditional PSO (particle swarm optimization) algorithm and general heuristic algorithm. The hybrid algorithm can effectively improve the optimization efficiency of VRPTW, lay a foundation for solving large-scale VRPTW, and provide new research ideas and methods. At the same time, the results fully show that the algorithm in this paper has certain advantages in performance, and it can be applied to logistics delivery route optimization.

A Bio-Inspired Cluster Optimization Schema for Efficient Routing in Vehicular Ad Hoc Networks (VANETs)

Vehicular ad hoc networks (VANETs) are vital to many Intelligent Transportation System (ITS)-enabled technologies, including efficient traffic control, media applications, and encrypted financial transactions. Due to an increase in traffic, vehicular network topology is constantly changing, and sparse vehicle distribution (on highways) hinders network scalability. Thus, there is a challenge for all vehicles (in the network) to maintain a stable route, which would increase network instability. Concerning IoT-based network transportation, this study proposes a bio-inspired, cluster-based algorithm for routing, i.e., the intelligent, probability-based, and nature-inspired whale optimization algorithm (p-WOA), which produces cluster formation in vehicular communication. Various parameters, such as communication range, number of nodes, velocity, and route along the highway were considered, and their probaabilities were incorporated into the fitness function, hence resulting in randomness reduction. Results were compared to existing methods such as Ant Lion Optimizer (ALO) and Grey Wolf Optimization (GWO), demonstrating that the developed p-WOA technique produces an optimal number of cluster heads (CH). The results achieved by calculating the Packet Delivery Ratio (PDR), average throughput, and latency demonstrate the superiority of the proposed method over other well-established methodologies (ALO and GWO). This study confirms statistically that VANETs employing ITS applications optimize their clusters by a factor of 75, which has the twin benefits of decreasing communication costs and routing overhead and extending the life of the cluster as a whole.