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Journal articles on the topic 'Routing optimization'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Tariq, Rehan, Zeshan Iqbal, and Farhan Aadil. "IMOC: Optimization Technique for Drone-Assisted VANET (DAV) Based on Moth Flame Optimization." Wireless Communications and Mobile Computing 2020 (November 7, 2020): 1–29. http://dx.doi.org/10.1155/2020/8860646.

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Technology advancement in the field of vehicular ad hoc networks (VANETs) improves smart transportation along with its many other applications. Routing in VANETs is difficult as compared to mobile ad hoc networks (MANETs); topological constraints such as high mobility, node density, and frequent path failure make the VANET routing more challenging. To scale complex routing problems, where static and dynamic routings do not work well, AI-based clustering techniques are introduced. Evolutionary algorithm-based clustering techniques are used to solve such routing problems; moth flame optimization

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2

Fathima, H., and A. Syed Musthafa. "Optimization based Routing Algorithms." International Journal of Applied Research on Information Technology and Computing 5, no. 1 (2014): 55. http://dx.doi.org/10.5958/j.0975-8089.5.1.006.

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3

Jaillet, Patrick, Jin Qi, and Melvyn Sim. "Routing Optimization Under Uncertainty." Operations Research 64, no. 1 (2016): 186–200. http://dx.doi.org/10.1287/opre.2015.1462.

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4

Isa, Norulhidayah. "Traffic Routing Optimization using Ant Colony Optimization." International Journal of Advanced Trends in Computer Science and Engineering 8, no. 1.5 (2019): 81–86. http://dx.doi.org/10.30534/ijatcse/2019/1781.52019.

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5

Kumar, Rohit. "IMPLEMENTING POWER OPTIMIZATION IN WSN USING A PRAGMATIC APPROACH." International Journal of Engineering Science and Humanities 14, no. 1 (2024): 15–27. http://dx.doi.org/10.62904/qqnddg76.

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Routing involves transferring data from one point to another within a network of interconnected devices. During the transmission of data, there is at least one middle nodethat is encountered in the network. Essentially, this concept includes two main tasks: findingthebest routing paths and sending packets across a network. The process of moving packets across an internetwork is known as packet switching, and while it is direct, determiningthepath can be quite complicated. Routing protocols utilize various metrics as a standardtodetermine the best path for routing packets to their destination,

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6

Bahadur Singh Asha, Sankalp. "Optimization of Routing Protocol in MANET using GA." International Journal of Science and Research (IJSR) 1, no. 2 (2012): 23–26. http://dx.doi.org/10.21275/ijsr11120221.

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7

Hussain, S. Jahir, and Ramalingam Sugumar. "Enhanced Whale Optimization Algorithm for Multi-Objective Node Disjoint Routing." Indian Journal Of Science And Technology 17, no. 21 (2024): 2138–49. http://dx.doi.org/10.17485/ijst/v17i21.3092.

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Objectives: The research goal is to identify node-disjoint pathways maximizing path lifetime, delivery ratio, etc. while establishing several node-disjoint paths to optimize routing and enhance interference reduction and PDR efficiency. Methods: In the MANET, the Enhanced Whale Optimization Algorithm (EWOA) is utilized to discover the most secure routing path. The dataset used in this research comprises simulated scenarios representing various network conditions and configurations, allowing for a comprehensive evaluation of the proposed work. The software employed for simulation and analysis i

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Sharma, Bhanu, and Amar Singh. "Routing in Wireless Mesh Network: A New Metaheuristic Based Routing Approach." Journal of University of Shanghai for Science and Technology 23, no. 08 (2021): 711–19. http://dx.doi.org/10.51201/jusst/21/08460.

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Routing is a challenging issue of WMNs due to the dynamic nature of the network. In WMNs, a node can leave or join the network at any time. So, there is a need for an efficient routing algorithm in WMNs that should quickly discover the path. The development of different networking environments has a significant effect on WMNs routing. This paper proposes a new Butterfly Optimization algorithm (BOA) based routing approach for Wireless Mesh Networks. The proposed BOA routing approach was implemented using MATLAB, and its performance was compared with Ad Hoc On-Demand Distance Vector(AODV), Ant C

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Nirmala, bai K.S, and Subramanyam M.V. "Optimization of Wireless Sensor Networks Using the ANT Colony Optimization." Journal of Scholastic Engineering Science and Management (JSESM), A Peer Reviewed Universities Refereed Multidisciplinary Research Journal 2, no. 7 (2023): 27–32. https://doi.org/10.5281/zenodo.15192879.

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This paper proposes an ant colony optimization-based routing algorithm for wireless sensor networks. The algorithm incorporates link quality into pheromone formation and supports multiple routes, which are characteristics of an ant colony algorithm. When routing is chosen, the probability of the node being chosen as the next hop is calculated based on the pheromone concentration on the route. Because ant colony optimization is self-organizing, dynamic, and multi-path, it is ideal for wireless sensor network routing. This algorithm has a low routing cost, good self-adaptation, and can handle mu

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10

Hua, Zhang Chun, Hua Xin, and Zhang Wei. "Logistics Distribution Routing Optimization Algorithm." Applied Mechanics and Materials 513-517 (February 2014): 1740–43. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.1740.

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Logistics distribution involves preparing goods in the distribution center or logistics node for most reasonable delivery according to the requirements of customers. Genetic algorithm is a random global search algorithm based on the principle of natural evolution. It can be a good solution to optimize the distribution routes. This paper combines genetic algorithm and the simulated annealing algorithm, to which memory device is added, in order to avoid best result losing in the crossover operator of the genetic algorithm. The experimental results show that a memory function with this genetic si

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11

Rani, Rekha. "Optimization of MANET Routing Protocol." Global Sci-Tech 8, no. 2 (2016): 77. http://dx.doi.org/10.5958/2455-7110.2016.00009.4.

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12

Li, Yihua, Qing Miao, and Bruce X. Wang. "U.S. Postal Airmail Routing Optimization." Transportation Research Record: Journal of the Transportation Research Board 2334, no. 1 (2013): 21–28. http://dx.doi.org/10.3141/2334-03.

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13

Rougier, J. L., D. Kofman, and A. Gravey. "Optimization of hierarchical routing protocols." Performance Evaluation 41, no. 2-3 (2000): 227–45. http://dx.doi.org/10.1016/s0166-5316(00)00009-2.

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14

Papatzanakis, Georgios I., Apostolos D. Papanikolaou, and Shukui Liu. "Optimization of routing considering uncertainties." Journal of Marine Science and Application 11, no. 1 (2012): 10–17. http://dx.doi.org/10.1007/s11804-012-1100-y.

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15

Sahoo, Surya, Seongbae Kim, Byung-In Kim, Bob Kraas, and Alexander Popov. "Routing Optimization for Waste Management." Interfaces 35, no. 1 (2005): 24–36. http://dx.doi.org/10.1287/inte.1040.0109.

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16

Zhang, Yan, Chunhui Yuan, and Jiang Wu. "Vehicle Routing Optimization of Instant Distribution Routing Based on Customer Satisfaction." Information 11, no. 1 (2020): 36. http://dx.doi.org/10.3390/info11010036.

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Since the actual factors in the instant distribution service scenario are not considered enough in the existing distribution route optimization, a route optimization model of the instant distribution system based on customer time satisfaction is proposed. The actual factors in instant distribution, such as the soft time window, the pay-to-order mechanism, the time for the merchant to prepare goods before delivery, and the deliveryman’s order combining, were incorporated in the model. A multi-objective optimization framework based on the total cost function and time satisfaction of the customer

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17

Bharany, Salil, Sandeep Sharma, Surbhi Bhatia, Mohammad Khalid Imam Rahmani, Mohammed Shuaib, and Saima Anwar Lashari. "Energy Efficient Clustering Protocol for FANETS Using Moth Flame Optimization." Sustainability 14, no. 10 (2022): 6159. http://dx.doi.org/10.3390/su14106159.

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FANET (flying ad-hoc networks) is currently a trending research topic. Unmanned aerial vehicles (UAVs) have two significant challenges: short flight times and inefficient routing due to low battery power and high mobility. Due to these topological restrictions, FANETS routing is considered more complicated than MANETs or VANETs. Clustering approaches based on artificial intelligence (AI) approaches can be used to solve complex routing issues when static and dynamic routings fail. Evolutionary algorithm-based clustering techniques, such as moth flame optimization, and ant colony optimization, c

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18

Park, Sejin, Hua Li, and Gunhak Lee. "Routing Optimization of Mobile Library Using Vehicle Routing Problem (VRP)." Journal of the Korean Cartographic Association 22, no. 3 (2022): 25–36. http://dx.doi.org/10.16879/jkca.2022.22.3.025.

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19

Quan-Hai Wang, Quan-Hai Wang, Shao-Feng Lan Quan-Hai Wang, Jing Feng Shao-Feng Lan, Yi-Qun Wang Jing Feng, and Wen-Bai Chen Yi-Qun Wang. "Improvement and Optimization of a Mobile Multi-agent AODV Routing Protocol." 電腦學刊 33, no. 4 (2022): 205–13. http://dx.doi.org/10.53106/199115992022083304017.

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<p>In the scenario of node failure in mobile multi-agent, Ad hoc On-demand Distance Vector (AODV) rout-ing protocol fails in network information transmission due to the damage of communication link. In addi-tion, the existing AODV multipath routing protocol node load balancing mechanism is imperfect. In order to solve the low network survivability caused by the above problems, an improved responsive multipath routing protocol MD_AODV (Multipath Destroy-resistance AODV) is proposed. The protocol is opti-mized in three aspects: multi-channel concurrency mechanism, node load balancing mecha

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20

Lei, Jun Cheng, Yan Peng Wu, and Wen Fei Zeng. "Optimization Approach for Multi-Stork Inventory Routing Problem." Advanced Materials Research 268-270 (July 2011): 1637–40. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.1637.

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Inventory routing problem is one of the key issues to achieve integrated management of logistics. Solving this problem effectively, we can improve vehicle utilization, and reduce distribution costs. This paper, concerning the problem in inventory routing of multi-variety, multi-vendor to multi-customers, proposed heuristic algorithm based on greedy rules. The core strategy of the algorithm is to choose circularly the current lowest unit cost routine ---Hamilton delivery routes. Simulation shows that the algorithm reduces the unloaded ratio of truck, raises the efficiency of truck delivery and

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21

Fan, Bing, Ying Zeng, and Liang Rui Tang. "Chaotic Clonal Genetic Algorithm for Routing Optimization." Advanced Materials Research 1046 (October 2014): 371–74. http://dx.doi.org/10.4028/www.scientific.net/amr.1046.371.

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Clonal operator which can reserve the elites is introduced in the selection step of traditional genetic algorithm (GA) to accelerate the local convergence speed. Chaotic search which is randomness and ergodicity is applied in crossover and mutation operators to avoid the algorithm stopping at a local extreme value. The above hybrid GA is called chaotic clonal GA (CCGA) which can overcome the instability of optimizing processes and results in traditional GA by the certainty of chaotic trajectory. The CCGA is applied to solve the problem of load balance routing in differentiated service networks

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22

Xue, RiXin, Peng Tang, and Shudong Fang. "Multiwing UAV Communication and Energy Consumption Optimization Based on Energy Optimization." Wireless Communications and Mobile Computing 2022 (March 12, 2022): 1–10. http://dx.doi.org/10.1155/2022/5403035.

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The UAV carries communication equipment to lift off as an aerial base station, which has the characteristics of flexibility, rapid deployment, etc. Reducing the energy consumption of UAVs is a key issue that needs to be urgently addressed in the current UAV field to build green UAVs. The transmission service rate adaptivity of UAV devices provides an effective way to optimize UAV energy consumption and improve UAV energy efficiency, and a global and distributed UAV energy optimization routing strategy based on routing algorithm is proposed in this paper. The strategy starts from the perspectiv

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23

Huo, Zhouhu, Niannian Sun, Chaofei Zhang, Bingbing Li, and Jixin Zhang. "A Co-Optimization Method for Analog IC Placement and Routing Based on Sequence Pairs and Random Forests." Electronics 14, no. 7 (2025): 1349. https://doi.org/10.3390/electronics14071349.

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In the physical design of integrated circuits (ICs), conventional methodologies treat placement and routing as two sequential and independent steps, resulting in placement adjustments that do not sufficiently account for routing requirements. Such decoupled optimization leads to various routing challenges, including congestion and excessive wirelength. To address these limitations, we propose a novel iterative co-optimization framework that simultaneously considers placement and routing. Our methodology establishes an interdependent relationship between placement and routing, where routing is

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24

Dempsey, Peter, and Alfons Schuster. "Swarm intelligence for network routing optimization." Journal of Telecommunications and Information Technology, no. 3 (September 30, 2005): 24–28. http://dx.doi.org/10.26636/jtit.2005.3.329.

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This paper presents the results of a comparative study of network routing approaches. Recent advances in the field suggest that swarm intelligence may offer a robust, high quality solution. The overall aim of the study was to develop a framework to facilitate the empirical evaluation of a swarm intelligence routing approach compared to a conventional static and dynamic routing approach. This paper presents a framework for the simulation of computer networks, collection of performance statistics, generation and reuse of network topologies and traffic patterns.

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25

Okoro, R., O. C. Ubadike, A. J. Onumanyi, and A. M. Aibinu. "Routing optimization in a wireless network using genetic algorithm and ILA routing metric." Nigerian Journal of Technology 40, no. 5 (2022): 938–46. http://dx.doi.org/10.4314/njt.v40i5.19.

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Wireless access technology is a popular means of delivering various telecommunication services to users in Nigeria. Wireless Mesh Networking and Wireless Sensor Networking have found useful applications in wireless access systems, surveillance and infrastructure Management. A key problem associated with this technology is performance degradation in terms of Quality of Service (QoS). Achieving good QoS in Wireless Networks design involves implementing a robust routing optimization technique. Genetic Algorithm (GA) technique in NS2 network simulator was used to implement routing optimization in

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26

Morsidi, Farid, and Shir Li Wang. "Meta-Analysis on Substantive Mechanics for Maximizing Productivity and Cost Reciprocity in Routing Optimization." Applied Mathematics and Computational Intelligence (AMCI) 14, no. 2 (2025): 55–81. https://doi.org/10.58915/amci.v14i2.918.

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In the following article, several conjoining features are discussed for routing optimization by incorporating cost-approximating measures derived from cost-resource allocation reciprocity. To maximize productivity and cost efficiency, the deployment of computational intelligence applications over scheduling systems is coordinated across the optimization process. Several relevant co-works previously envisioning this topic's broad context are discussed as a conjoining point for correlating the expositions of annotating a highly proficient routing system architecture via an emphasis on planning t

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27

Sonia and Banita. "Performance optimization of manet." World Journal of Advanced Engineering Technology and Sciences 13, no. 2 (2024): 555–59. https://doi.org/10.30574/wjaets.2024.13.2.0618.

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Mobile networking refers to the technology utilized to transmit speech and data between specific mobile network nodes across wireless channels. In general, ”mobile” refers to the purposeful, lightweight, and portable technologies that moviegoers may carry. A Mobile Ad Hoc Network (MANET) protocol requires proper settings to perform data transmission optimally. To overcome this problem, it is necessary to select the correct routing protocol and use the routing protocol’s default parameter values.. The performance metrics used for measuring performance were Packet Delivery Ratio (PDR), throughpu

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28

YE, Ping, Yi-Zhong LI, and Qin XIA. "Delay Optimization Based Overlay Routing Strategy." Chinese Journal of Computers 33, no. 1 (2010): 36–44. http://dx.doi.org/10.3724/sp.j.1016.2010.00036.

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29

Zhou, Jingjing, Weiming Wang, and Xudong Zhu. "Research of the ForCES Routing Optimization." Research Journal of Applied Sciences, Engineering and Technology 5, no. 5 (2013): 1573–77. http://dx.doi.org/10.19026/rjaset.5.4906.

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30

Wang, Kai, Lu Zhen, Jun Xia, Roberto Baldacci, and Shuaian Wang. "Routing Optimization with Generalized Consistency Requirements." Transportation Science 56, no. 1 (2022): 223–44. http://dx.doi.org/10.1287/trsc.2021.1072.

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The consistent vehicle routing problem (ConVRP) aims to design synchronized routes on multiple days to serve a group of customers while minimizing the total travel cost. It stipulates that customers should be visited at roughly the same time (time consistency) by several familiar drivers (driver consistency). This paper generalizes the ConVRP for any level of driver consistency and additionally addresses route consistency, which means that each driver can traverse at most a certain proportion of different arcs of routes on planning days, which guarantees route familiarity. To solve this proble

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31

Rozner, Eric, Mi Kyung Han, Lili Qiu, and Yin Zhang. "Model-Driven Optimization of Opportunistic Routing." IEEE/ACM Transactions on Networking 21, no. 2 (2013): 594–609. http://dx.doi.org/10.1109/tnet.2012.2205701.

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32

Cho, Minsik, Hua Xiang, Ruchir Puri, and David Z. Pan. "Track Routing and Optimization for Yield." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 27, no. 5 (2008): 872–82. http://dx.doi.org/10.1109/tcad.2008.917589.

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33

Luo, Xiangang, Jingmin Tu, and Lu Huang. "Optimization of Express Delivery Routing Problem." TELKOMNIKA (Telecommunication Computing Electronics and Control) 14, no. 3A (2016): 380. http://dx.doi.org/10.12928/telkomnika.v14i3a.4420.

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34

Huang, Min, Liang Ren, Loo Hay Lee, and Xingwei Wang. "4PL routing optimization under emergency conditions." Knowledge-Based Systems 89 (November 2015): 126–33. http://dx.doi.org/10.1016/j.knosys.2015.06.023.

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35

WATARI, M., T. ERNST, R. WAKIKAWA, and J. MURAI. "Routing Optimization for Nested Mobile Networks." IEICE Transactions on Communications E89-B, no. 10 (2006): 2786–93. http://dx.doi.org/10.1093/ietcom/e89-b.10.2786.

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36

Awerbuch, Baruch, and Robert Kleinberg. "Online linear optimization and adaptive routing." Journal of Computer and System Sciences 74, no. 1 (2008): 97–114. http://dx.doi.org/10.1016/j.jcss.2007.04.016.

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37

Borndörfer, Ralf, Heide Hoppmann, and Marika Karbstein. "Passenger routing for periodic timetable optimization." Public Transport 9, no. 1-2 (2016): 115–35. http://dx.doi.org/10.1007/s12469-016-0132-0.

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38

Christodoulou, Symeon E., and Georgios Ellinas. "Pipe Routing through Ant Colony Optimization." Journal of Infrastructure Systems 16, no. 2 (2010): 149–59. http://dx.doi.org/10.1061/(asce)1076-0342(2010)16:2(149).

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39

Buslaeva, L. T. "Stability of a routing optimization algorithm." Cybernetics and Systems Analysis 33, no. 5 (1997): 754–56. http://dx.doi.org/10.1007/bf02667202.

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40

Ren, Kui, Wenjing Lou, Kai Zeng, Feng Bao, Jianying Zhou, and Robert H. Deng. "Routing optimization security in mobile IPv6." Computer Networks 50, no. 13 (2006): 2401–19. http://dx.doi.org/10.1016/j.comnet.2005.09.019.

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41

Rashed, Thamir, Emad Shehab, and Dalia Kamal. "Routing Optimization of Mobile Network System." Engineering and Technology Journal 27, no. 8 (2009): 1497–507. http://dx.doi.org/10.30684/etj.27.8.6.

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42

R. Sivaranjani, Dr. R. Shankar, and Dr. S. Duraisamy. "Energy Efficient and MANET Routing Optimization." International Journal of Advanced Networking and Applications 16, no. 05 (2025): 6596–601. https://doi.org/10.35444/ijana.2025.16509.

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MANETs can dynamically build a network in wireless networks without the need for a central hub or other infrastructure. For situations like disaster relief or military activities, these features will be useful. The network's stability and capabilities are impacted by the security and routing issues that arise from the lack of a central authority. In order to effectively address the problems, a suggested design takes the power factor into description when choosing routes. Utilizing the NS-2 simulator, these are simulated and integrated utilized and widely used AODV routing protocol. To ascertai

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43

张, 宇. "Satellite Network Intelligent Routing Optimization Mechanism." Software Engineering and Applications 14, no. 02 (2025): 332–45. https://doi.org/10.12677/sea.2025.142030.

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44

B, Vatchala, and G. Preethi. "Enhanced Ant Colony Optimization Based Secured Routing in Cloud Computing." International Journal of Science and Research (IJSR) 10, no. 7 (2021): 231–39. https://doi.org/10.21275/sr21616115833.

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45

Zhang, Zexi. "The Truck Platooning Routing Optimization Model Based on Multicommodity Network Flow Theory." Journal of Advanced Transportation 2023 (January 7, 2023): 1–12. http://dx.doi.org/10.1155/2023/6906655.

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Truck platooning has been identified as an emerging and promising operational technology with the advantages of fuel consumption savings and carbon emissions reductions. We formulate the truck platooning routing optimization problem as a multi-commodity network flow problem from a transportation optimization and scheduling perspective. Based on fuel consumption savings generated through the reduction of aerodynamic drag by the formation of truck platooning, the route of each truck is also set to be a decision variable needing settlement to facilitate the formation of truck platooning to maximi

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46

Chen, Ai Ling. "Logistics Distribution Vehicle Routing Problem with Time Windows." Advanced Materials Research 468-471 (February 2012): 2047–51. http://dx.doi.org/10.4028/www.scientific.net/amr.468-471.2047.

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Vehicle routing optimization problem is one of the major research topics in logistics distribution field. Suitable vehicle routing selection is vital to reduce the logistics cost. The paper presents a hybrid optimization method to solve the vehicle routing problem with time windows. In the hybrid optimization method, discrete particle swarm optimization algorithm is used to assign the customers on routes and simulated annealing (SA) algorithm to avoid becoming trapped in local optimum. The simulation results have shown that the proposed method is feasible and effective for the vehicle routing

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47

Shashidhar, P. K., T. C. Thanuja, and Rajashekar Kunabeva. "Adaptive RPL Routing Optimization Model for Multimedia Data Transmission using IOT." Indian Journal Of Science And Technology 17, no. 5 (2024): 436–50. http://dx.doi.org/10.17485/ijst/v17i5.2627.

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Objectives: The main objectives of this research endeavor encompass the development of the Adaptive RPL Optimization (ARPLO) model to enhance data transmission efficiency within IoT networks. This includes constructing a grid-based network structure optimized for data transfer, selecting the most suitable nodes as grid head nodes to maximize network lifespan while minimizing energy consumption, implementing an innovative objective function-driven approach to optimize parent node selection, and integrating an Adaptive Deep Neural Network (ADNN) to accurately classify medical data. Methods: The

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48

Xiao, Jing, Yang Liu, Hu Qin, Chaoqun Li, and Jie Zhou. "A Novel QoS Routing Energy Consumption Optimization Method Based on Clone Adaptive Whale Optimization Algorithm in IWSNs." Journal of Sensors 2021 (April 22, 2021): 1–14. http://dx.doi.org/10.1155/2021/5579252.

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Routing requests in industrial wireless sensor networks (IWSNs) are always restricted by QoS. Therefore, finding a high-quality routing path is a key problem. In this paper, a clone adaptive whale optimization algorithm (CAWOA) is designed for reducing the routing energy consumption of IWSNs with QoS constraints, and a novel clone operator is proposed. More importantly, CAWOA innovatively adopts a discrete binary-based routing coding method, which provides strong support for optimal routing schemes. In addition, a novel routing model of IWSNs combined with QoS constraints has been designed, wh

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49

Tlili, Takwa, Sami Faiz, and Saoussen Krichen. "Integration of GIS and Optimization Routines for the Vehicle Routing Problem." International Journal of Chaos, Control, Modelling and Simulation 2, no. 2 (2013): 9–17. http://dx.doi.org/10.5121/ijccms.2013.2202.

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Takwa, Tlili, Faiz Sami, and Saoussen Krichen and. "INTEGRATION OF GIS AND OPTIMIZATION ROUTINES FOR THE VEHICLE ROUTING PROBLEM." International Journal of Chaos, Control, Modelling and Simulation (IJCCMS) 2, no. 2 (2013): 09–17. https://doi.org/10.5281/zenodo.6837214.

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This paper evokes the vehicle routing problem (VRP) which aims to determine the minimum total cost pathways for a fleet of heterogeneous vehicles to deliver a set of customers' orders. The inability of optimization algorithms alone to fully satisfy the needs of logistic managers become obvious in transportation field due to the spatial nature of such problems. In this context, we couple a geographical information system (GIS) with a metaheuristic to handle the VRP efficiently then generate a geographical solution instead of the numerical solution. A real-case instance in a Tunisian region

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