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Fault-Tolerant Routing Algorithm for Network-on-Chip Based on Dynamic XY Routing 认领 引用 被引量：1
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作者 LI Xiaohui CAO Yang +1 位作者 WANG Liwei CAI Tian 《Wuhan University Journal of Natural Sciences》 CAS 2009年第4期343-348,共6页
In order to ensure the reliability of network-on-chip （NoC） under faulty circumstance, a dynamic fault tolerant routing algorithm is proposed. This algorithm can implement detour routing when there are both static a... In order to ensure the reliability of network-on-chip （NoC） under faulty circumstance, a dynamic fault tolerant routing algorithm is proposed. This algorithm can implement detour routing when there are both static and dynamic permanent faults in the network. That means the packet is able to move around the fanlts to the destination with a non-minimum path. In addition, the multi-level congestion control mechanism gives the algorithm the ability to distribute the load over the whole network and to avoid hotspots around the faults. Simulation results demonstrate the advantage of the proposed routing algorithm in terms of average packet latency and packet loss rate compared with negative-first routing algo- rithm and DyAD routing algorithm in the presence of permanent faults. For the proposed algorithm, it can get much less average packet latency and lead to less than 20% packet loss rate. 展开更多
关键词 network-on-chip （NoC） fault-tolerant routing algo- rithm congestion control
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Grey Wolf Optimizer for Cluster-Based Routing in Wireless Sensor Networks:A Methodological Survey 认领 引用
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作者 Mohammad Shokouhifar Fakhrosadat Fanian +4 位作者 Mehdi Hosseinzadeh Aseel Smerat Kamal M.Othman Abdulfattah Noorwali Esam Y.O.Zafar 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第1期191-255,共65页
Wireless Sensor Networks(WSNs)have become foundational in numerous real-world applications,ranging from environmental monitoring and industrial automation to healthcare systems and smart city development.As these netw... Wireless Sensor Networks(WSNs)have become foundational in numerous real-world applications,ranging from environmental monitoring and industrial automation to healthcare systems and smart city development.As these networks continue to grow in scale and complexity,the need for energy-efficient,scalable,and robust communication protocols becomes more critical than ever.Metaheuristic algorithms have shown significant promise in addressing these challenges,offering flexible and effective solutions for optimizing WSN performance.Among them,the Grey Wolf Optimizer(GWO)algorithm has attracted growing attention due to its simplicity,fast convergence,and strong global search capabilities.Accordingly,this survey provides an in-depth review of the applications of GWO and its variants for clustering,multi-hop routing,and hybrid cluster-based routing in WSNs.We categorize and analyze the existing GWO-based approaches across these key network optimization tasks,discussing the different problem formulations,decision variables,objective functions,and performance metrics used.In doing so,we examine standard GWO,multi-objective GWO,and hybrid GWO models that incorporate other computational intelligence techniques.Each method is evaluated based on how effectively it addresses the core constraints of WSNs,including energy consumption,communication overhead,and network lifetime.Finally,this survey outlines existing gaps in the literature and proposes potential future research directions aimed at enhancing the effectiveness and real-world applicability of GWO-based techniques for WSN clustering and routing.Our goal is to provide researchers and practitioners with a clear,structured understanding of the current state of GWO in WSNs and inspire further innovation in this evolving field. 展开更多
关键词 Wireless sensor networks data transmission energy efficiency lifetime clustering routing optimization metaheuristic algorithms grey wolf optimizer
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A Memetic Algorithm for Solving UAV Routing Problems with Profits 认领 引用
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作者 Siliang Hua Jian Xu +3 位作者 Huiguo Zhang Qian Zhang Lifeng Qin Lixing Hua 《Instrumentation》 2025年第4期48-56,共9页
This study addresses the Unmanned Aerial Vehicle routing problems with profits,which requires balancing mission profit,path efficiency,and battery health under complex constraints,particularly the nonlinear degradatio... This study addresses the Unmanned Aerial Vehicle routing problems with profits,which requires balancing mission profit,path efficiency,and battery health under complex constraints,particularly the nonlinear degradation of batteries.This paper proposes an enhanced memetic algorithm by integrating adaptive local search and a dynamic population management mechanism.The algorithm employs a hybrid initialization strategy to generate high-quality initial solutions.It incorporates an improved linear crossover operator to preserve beneficial path characteristics and introduces dynamically probability-controlled local search to optimize solution quality.To enhance global exploration capability,a population screening mechanism based on solution similarity and a population restart strategy simulating biological mass extinction are designed.Extensive experiments conducted on standard Tsiligirides's and Chao's datasets demonstrate the algorithm's robust performance across scenarios ranging from 21 to 66 nodes and time constraints spanning 5 to 130 minutes.The algorithm attains 95%accuracy relative to maximum total score within 30 iterations,surpassing 99%accuracy after 100 iterations.Its comprehensive performance significantly surpasses that of traditional heuristic methods.The proposed method provides an efficient and robust solution for Unmanned Aerial Vehicle routing planning under intricate constraints. 展开更多
关键词 memetic algorithm unmanned aerial vehicle routing orienteering problem
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iPAFAR: An Adaptive Pareto-Based NS-AAA Energy-Stable Fuzzy Clustering and Routing Framework for Smart City IoT-Enabled WSNs 认领 引用
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作者 Bhanu Talwar Puneet Thapar +3 位作者 Tahani Alsubait Mai Alduailij Ateeq Ur Rehman Salil Bharany 《Computers, Materials & Continua》 SCIE EI 2026年第8期785-805,共21页
Wireless Sensor Networks(WSNs)play a vital role in smart city Internet of Things(IoT)applications,including environmental monitoring,intelligent transportation,and infrastructure management.However,limited battery cap... Wireless Sensor Networks(WSNs)play a vital role in smart city Internet of Things(IoT)applications,including environmental monitoring,intelligent transportation,and infrastructure management.However,limited battery capacity,uneven energy consumption,and inefficient clustering and routing mechanisms significantly reduce network lifetime,reliability,and scalability,especially in large-scale IoT deployments.Traditional routing protocols often rely on single-objective optimization or static clustering strategies,which fail to maintain long-term energy balance and stable communication performance.To address these challenges,this paper proposes iPAFAR,a Pareto-based multi-objective clustering and routing framework designed for IoT-enabled WSNs.The proposed model formulates cluster-head selection as a multi-objective optimization problem that considers residual energy,node centrality,load variance,and fairness.A Non-Dominated Sorting Artificial Algae Algorithm(NS-AAA)is used to obtain Pareto-optimal cluster-head configurations,followed by a fuzzy inference system for refined decision-making.To ensure long-term energy stability,a Lyapunov-based routing model is incorporated,and an adaptive re-clustering mechanism is introduced to reduce unnecessary control overhead under dynamic network conditions.The performance of the proposed framework is evaluated through MATLAB-based simulations and compared with existing protocols,including LEACH-M,ME-LEACH,FQA,MKNDPC,RANP-PSO,and BKA-TOA.Experimental results show that iPAFAR achieves approximately 40%lower end-to-end delay,15%–20%higher packet delivery ratio,and 45%–50%improvement in residual energy while maintaining nearly twice the number of active nodes after 1000 simulation rounds.These results confirm that the proposed framework provides improved energy efficiency,load balancing,and routing stability,making it suitable for long-term smart city IoT deployments. 展开更多
关键词 Wireless sensor networks smart cities multi-objective optimization artificial algae algorithm energy-aware clustering pareto optimization IoT routing
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CONNECTIVITY OF CARTESIAN PRODUCT DIGRAPHS AND FAULT-TOLERANT ROUTINGS OF GENERALIZED HYPERCUBE 认领 引用 被引量：2
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作者 XU JUNMING 《Applied Mathematics(A Journal of Chinese Universities)》 1998年第2期179-187,共9页
Abstract In this paper, the problem of fault tolerant routings in fault tolerant networks is considered. A routing in a network assigns to each ordered pair of nodes a fixed path. All communication among nodes must go... Abstract In this paper, the problem of fault tolerant routings in fault tolerant networks is considered. A routing in a network assigns to each ordered pair of nodes a fixed path. All communication among nodes must go on this routing. When either a node or a link in a fault tolerant network fails, the communication from one node to another using this faulty element must be sent via one or more intermediate nodes along a sequence of paths determined by this routing. An important and practical problem is how to choose a routing in the network such that intermediate nodes to ensure communication are small for any fault set. Let C d be a directed cycle of order d . In this paper. The author first discusses connectivity of Cartesian product digraphs, then proves that the Cartesian product digraph C d 1 ×C d 2 ×...×C d n (d i≥2,1≤i≤n) has a routing such that at most one intermediate node is needed to ensure transmission of messages among all non faulty nodes so long as the number of faults is less than n . This is a generalization of Dolev et al's result for the n dimensional cube. 展开更多
关键词 Fault-tolerant networks routings digraphs hypercube connectivity diameter
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Joint Optimization of Routing and Resource Allocation in Decentralized UAV Networks Based on DDQN and GNN 认领 引用
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作者 Nawaf Q.H.Othman YANG Qinghai JIANG Xinpei 《电讯技术》 北大核心 2026年第1期1-10,共10页
Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combinin... Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combining double deep Q-networks(DDQNs)and graph neural networks(GNNs)for joint routing and resource allocation.The framework uses GNNs to model the network topology and DDQNs to adaptively control routing and resource allocation,addressing interference and improving network performance.Simulation results show that the proposed approach outperforms traditional methods such as Closest-to-Destination(c2Dst),Max-SINR(mSINR),and Multi-Layer Perceptron(MLP)-based models,achieving approximately 23.5% improvement in throughput,50% increase in connection probability,and 17.6% reduction in number of hops,demonstrating its effectiveness in dynamic UAV networks. 展开更多
关键词 decentralized UAV network resource allocation routing algorithm GNN DDQN DRL
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Multi-QoS routing algorithm based on reinforcement learning for LEO satellite networks 认领 引用 被引量：1
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作者 ZHANG Yifan DONG Tao +1 位作者 LIU Zhihui JIN Shichao 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2025年第1期37-47,共11页
Low Earth orbit(LEO)satellite networks exhibit distinct characteristics,e.g.,limited resources of individual satellite nodes and dynamic network topology,which have brought many challenges for routing algorithms.To sa... Low Earth orbit(LEO)satellite networks exhibit distinct characteristics,e.g.,limited resources of individual satellite nodes and dynamic network topology,which have brought many challenges for routing algorithms.To satisfy quality of service(QoS)requirements of various users,it is critical to research efficient routing strategies to fully utilize satellite resources.This paper proposes a multi-QoS information optimized routing algorithm based on reinforcement learning for LEO satellite networks,which guarantees high level assurance demand services to be prioritized under limited satellite resources while considering the load balancing performance of the satellite networks for low level assurance demand services to ensure the full and effective utilization of satellite resources.An auxiliary path search algorithm is proposed to accelerate the convergence of satellite routing algorithm.Simulation results show that the generated routing strategy can timely process and fully meet the QoS demands of high assurance services while effectively improving the load balancing performance of the link. 展开更多
关键词 low Earth orbit(LEO)satellite network reinforcement learning multi-quality of service(QoS) routing algorithm
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ON THE FAULT-TOLERANT ROUTING IN DISTRIBUTED LOOP NETWORKS 认领 引用
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作者 Liu Huanping Yang Yixian 《Journal of Electronics(China)》 2000年第1期84-89,共6页
Based on their "Theorem 2", an O(δ)-time algorithm of searching for the shortest path between each pair of nodes in a double loop network was proposed by K.Mukhopadyaya, et al.(1995). While, unfortunately, ... Based on their "Theorem 2", an O(δ)-time algorithm of searching for the shortest path between each pair of nodes in a double loop network was proposed by K.Mukhopadyaya, et al.(1995). While, unfortunately, it will be proved that both "Theorem 2" and its proof are in error. A new and more faster O(△)-time, △≤δ, algorithm will be presented in this paper. 展开更多
关键词 Double loop networks Shortest path routing Algorithm
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Load Balancing Routing Algorithm Based on Extended Link States in LEO Constellation Network 认领 引用 被引量：20
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作者 Chaoying Dong Xin Xu +1 位作者 Aijun Liu Xiaohu Liang 《China Communications》 SCIE CSCD 2022年第2期247-260,共14页
As an important part of satellite communication network,LEO satellite constellation network is one of the hot research directions.Since the nonuniform distribution of terrestrial services may cause inter-satellite lin... As an important part of satellite communication network,LEO satellite constellation network is one of the hot research directions.Since the nonuniform distribution of terrestrial services may cause inter-satellite link congestion,improving network load balancing performance has become one of the key issues that need to be solved for routing algorithms in LEO network.Therefore,by expanding the range of available paths and combining the congestion avoidance mechanism,a load balancing routing algorithm based on extended link states in LEO constellation network is proposed.Simulation results show that the algorithm achieves a balanced distribution of traffic load,reduces link congestion and packet loss rate,and improves throughput of LEO satellite network. 展开更多
关键词 LEO satellite routing algorithm congestion avoidance load balancing
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Application of Improved Multi-Objective Ant Colony Optimization Algorithm in Ship Weather Routing 认领 引用 被引量：13
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作者 ZHANG Guangyu WANG Hongbo +2 位作者 ZHAO Wei GUAN Zhiying LI Pengfei 《Journal of Ocean University of China》 SCIE CAS CSCD 2021年第1期45-55,共11页
This paper presents a novel intelligent and effective method based on an improved ant colony optimization(ACO)algorithm to solve the multi-objective ship weather routing optimization problem,considering the navigation... This paper presents a novel intelligent and effective method based on an improved ant colony optimization(ACO)algorithm to solve the multi-objective ship weather routing optimization problem,considering the navigation safety,fuel consumption,and sailing time.Here the improvement of the ACO algorithm is mainly reflected in two aspects.First,to make the classical ACO algorithm more suitable for long-distance ship weather routing and plan a smoother route,the basic parameters of the algorithm are improved,and new control factors are introduced.Second,to improve the situation of too few Pareto non-dominated solutions generated by the algorithm for solving multi-objective problems,the related operations of crossover,recombination,and mutation in the genetic algorithm are introduced in the improved ACO algorithm.The final simulation results prove the effectiveness of the improved algorithm in solving multi-objective weather routing optimization problems.In addition,the black-box model method was used to study the ship fuel consumption during a voyage;the model was constructed based on an artificial neural network.The parameters of the neural network model were refined repeatedly through the historical navigation data of the test ship,and then the trained black-box model was used to predict the future fuel consumption of the test ship.Compared with other fuel consumption calculation methods,the black-box model method showed higher accuracy and applicability. 展开更多
关键词 multi-objective optimization weather routing ACO algorithm fuel consumption
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Solving material distribution routing problem in mixed manufacturing systems with a hybrid multi-objective evolutionary algorithm 认领 引用 被引量：7
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作者 高贵兵 张国军 +2 位作者 黄刚 朱海平 顾佩华 《Journal of Central South University》 SCIE EI CAS 2012年第2期433-442,共10页
The material distribution routing problem in the manufacturing system is a complex combinatorial optimization problem and its main task is to deliver materials to the working stations with low cost and high efficiency... The material distribution routing problem in the manufacturing system is a complex combinatorial optimization problem and its main task is to deliver materials to the working stations with low cost and high efficiency. A multi-objective model was presented for the material distribution routing problem in mixed manufacturing systems, and it was solved by a hybrid multi-objective evolutionary algorithm (HMOEA). The characteristics of the HMOEA are as follows: 1) A route pool is employed to preserve the best routes for the population initiation; 2) A specialized best?worst route crossover (BWRC) mode is designed to perform the crossover operators for selecting the best route from Chromosomes 1 to exchange with the worst one in Chromosomes 2, so that the better genes are inherited to the offspring; 3) A route swap mode is used to perform the mutation for improving the convergence speed and preserving the better gene; 4) Local heuristics search methods are applied in this algorithm. Computational study of a practical case shows that the proposed algorithm can decrease the total travel distance by 51.66%, enhance the average vehicle load rate by 37.85%, cut down 15 routes and reduce a deliver vehicle. The convergence speed of HMOEA is faster than that of famous NSGA-II. 展开更多
关键词 material distribution routing problem multi-objective optimization evolutionary algorithm local search
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An Overview and Experimental Study of Learning-Based Optimization Algorithms for the Vehicle Routing Problem 认领 引用 被引量：11
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作者 Bingjie Li Guohua Wu +2 位作者 Yongming He Mingfeng Fan Witold Pedrycz 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2022年第7期1115-1138,共24页
The vehicle routing problem(VRP)is a typical discrete combinatorial optimization problem,and many models and algorithms have been proposed to solve the VRP and its variants.Although existing approaches have contribute... The vehicle routing problem(VRP)is a typical discrete combinatorial optimization problem,and many models and algorithms have been proposed to solve the VRP and its variants.Although existing approaches have contributed significantly to the development of this field,these approaches either are limited in problem size or need manual intervention in choosing parameters.To solve these difficulties,many studies have considered learning-based optimization(LBO)algorithms to solve the VRP.This paper reviews recent advances in this field and divides relevant approaches into end-to-end approaches and step-by-step approaches.We performed a statistical analysis of the reviewed articles from various aspects and designed three experiments to evaluate the performance of four representative LBO algorithms.Finally,we conclude the applicable types of problems for different LBO algorithms and suggest directions in which researchers can improve LBO algorithms. 展开更多
关键词 End-to-end approaches learning-based optimization(LBO)algorithms reinforcement learning step-by-step approaches vehicle routing problem(VRP)
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Congestion aware routing algorithm for delay-disruption tolerance networks 认领 引用 被引量：5
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作者 TAO Yong GONG Zheng-hu +1 位作者 LIN Ya-ping ZHOU Si-wang 《Journal of Central South University》 SCIE EI CAS 2011年第1期133-139,共7页
There were many contradictory evaluation criteria to select next-hop in the delay-disruption tolerance networks(DTN).To solve this problem,an attribute hierarchical model was proposed,in which the predefined criteria ... There were many contradictory evaluation criteria to select next-hop in the delay-disruption tolerance networks(DTN).To solve this problem,an attribute hierarchical model was proposed,in which the predefined criteria were summarized as static identity attributes,forwarding desire attributes and delivery capability attributes(IDC).Based on this model,a novel multi-attributes congestion aware routing(MACAR)scheme with uncertain information for next-hop selection was presented,by adopting an decision theory to aggregate attributes with belief structure and computing partial ordering relations.The simulation results show that MACAR presents higher successful delivery rate,lower average delay and effectively alleviate congestion. 展开更多
关键词 delay-disruption tolerant network congestion control routing algorithm custody transfer
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Single Failure Routing Protection Algorithm in the Hybrid SDN Network 认领 引用 被引量：5
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作者 Haijun Geng Jiangyuan Yao Yangyang Zhang 《Computers, Materials & Continua》 SCIE EI 2020年第7期665-679,共15页
Loop free alternate(LFA)is a routing protection scheme that is currently deployed in commercial routers.However,LFA cannot handle all single network component failure scenarios in traditional networks.As Internet serv... Loop free alternate(LFA)is a routing protection scheme that is currently deployed in commercial routers.However,LFA cannot handle all single network component failure scenarios in traditional networks.As Internet service providers have begun to deploy software defined network(SDN)technology,the Internet will be in a hybrid SDN network where traditional and SDN devices coexist for a long time.Therefore,this study aims to deploy the LFA scheme in hybrid SDN network architecture to handle all possible single network component failure scenarios.First,the deployment of LFA scheme in a hybrid SDN network is described as a 0-1 integer linear programming(ILP)problem.Then,two greedy algorithms,namely,greedy algorithm for LFA based on hybrid SDN(GALFAHSDN)and improved greedy algorithm for LFA based on hybrid SDN(IGALFAHSDN),are proposed to solve the proposed problem.Finally,both algorithms are tested in the simulation environment and the real platform.Experiment results show that GALFAHSDN and IGALFAHSDN can cope with all single network component failure scenarios when only a small number of nodes are upgraded to SDN nodes.The path stretch of the two algorithms is less than 1.36. 展开更多
关键词 Multipath routing network availability routing protection algorithm network failure hybrid SDN network
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A Nonuniform Clustering Routing Algorithm Based on an Improved K-Means Algorithm 认领 引用 被引量：4
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作者 Xinliang Tang Man Zhang +3 位作者 Pingping Yu Wei Liu Ning Cao Yunfeng Xu 《Computers, Materials & Continua》 SCIE EI 2020年第9期1725-1739,共15页
In a large-scale wireless sensor network(WSN),densely distributed sensor nodes process a large amount of data.The aggregation of data in a network can consume a great amount of energy.To balance and reduce the energy ... In a large-scale wireless sensor network(WSN),densely distributed sensor nodes process a large amount of data.The aggregation of data in a network can consume a great amount of energy.To balance and reduce the energy consumption of nodes in a WSN and extend the network life,this paper proposes a nonuniform clustering routing algorithm based on the improved K-means algorithm.The algorithm uses a clustering method to form and optimize clusters,and it selects appropriate cluster heads to balance network energy consumption and extend the life cycle of the WSN.To ensure that the cluster head(CH)selection in the network is fair and that the location of the selected CH is not concentrated within a certain range,we chose the appropriate CH competition radius.Simulation results show that,compared with LEACH,LEACH-C,and the DEEC clustering algorithm,this algorithm can effectively balance the energy consumption of the CH and extend the network life. 展开更多
关键词 WSN node energy consumption nonuniform clustering routing algorithm
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An improved estimation of distribution algorithm for multi-compartment electric vehicle routing problem 认领 引用 被引量：10
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作者 SHEN Yindong PENG Liwen LI Jingpeng 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2021年第2期365-379,共15页
The multi-compartment electric vehicle routing problem(EVRP)with soft time window and multiple charging types(MCEVRP-STW&MCT)is studied,in which electric multi-compartment vehicles that are environmentally friendl... The multi-compartment electric vehicle routing problem(EVRP)with soft time window and multiple charging types(MCEVRP-STW&MCT)is studied,in which electric multi-compartment vehicles that are environmentally friendly but need to be recharged in course of transport process,are employed.A mathematical model for this optimization problem is established with the objective of minimizing the function composed of vehicle cost,distribution cost,time window penalty cost and charging service cost.To solve the problem,an estimation of the distribution algorithm based on Lévy flight(EDA-LF)is proposed to perform a local search at each iteration to prevent the algorithm from falling into local optimum.Experimental results demonstrate that the EDA-LF algorithm can find better solutions and has stronger robustness than the basic EDA algorithm.In addition,when comparing with existing algorithms,the result shows that the EDA-LF can often get better solutions in a relatively short time when solving medium and large-scale instances.Further experiments show that using electric multi-compartment vehicles to deliver incompatible products can produce better results than using traditional fuel vehicles. 展开更多
关键词 multi-compartment vehicle routing problem electric vehicle routing problem(EVRP) soft time window multiple charging type estimation of distribution algorithm(EDA) Lévy flight
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Quality of Service Routing Strategy Using Supervised Genetic Algorithm 认领 引用 被引量：5
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作者 王兆霞 孙雨耕 +1 位作者 王志勇 沈花玉 《Transactions of Tianjin University》 EI CAS 2007年第1期48-52,共5页
A supervised genetic algorithm (SGA) is proposed to solve the quality of service (QoS) routing problems in computer networks. The supervised rules of intelligent concept are introduced into genetic algorithms (GAs) to... A supervised genetic algorithm (SGA) is proposed to solve the quality of service (QoS) routing problems in computer networks. The supervised rules of intelligent concept are introduced into genetic algorithms (GAs) to solve the constraint optimization problem. One of the main characteristics of SGA is its searching space can be limited in feasible regions rather than infeasible regions. The superiority of SGA to other GAs lies in that some supervised search rules in which the information comes from the problems are incorporated into SGA. The simulation results show that SGA improves the ability of searching an optimum solution and accelerates the convergent process up to 20 times. 展开更多
关键词 supervised genetic algorithm supervised search rules QoS routing
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Multi-Hop Routing Optimization Method Based on Improved Ant Algorithm for Vehicle to Roadside Network 认领 引用 被引量：2
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作者 Hao Dong Xiaohui Zhao +2 位作者 Liangdong Qu Xuefen Chi Xinyu Cui 《Journal of Bionic Engineering》 SCIE EI CSCD 2014年第3期490-496,共7页
This paper proposes a route optimization method to improve the performance of route selection in Vehicle Ad-hoc Network(VANET).A novel bionic swarm intelligence algorithm,which is called ant colony algorithm,was intro... This paper proposes a route optimization method to improve the performance of route selection in Vehicle Ad-hoc Network(VANET).A novel bionic swarm intelligence algorithm,which is called ant colony algorithm,was introduced into a traditional ad-hoc route algorithm named AODV.Based on the analysis of movement characteristics of vehicles and according to the spatial relationship between the vehicles and the roadside units,the parameters in ant colony system were modified to enhance the performance of the route selection probability rules.When the vehicle moves into the range of several different roadsides,it could build the route by sending some route testing packets as ants,so that the route table can be built by the reply information of test ants,and then the node can establish the optimization path to send the application packets.The simulation results indicate that the proposed algorithm has better performance than the traditional AODV algorithm,especially when the vehicle is in higher speed or the number of nodes increases. 展开更多
关键词 multi-hop routing optimization ant colony algorithm VANET bionic swarm intelligence algorithm
A Memetic Algorithm With Competition for the Capacitated Green Vehicle Routing Problem 认领 引用 被引量：11
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作者 Ling Wang Jiawen Lu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2019年第2期516-526,共11页
In this paper, a memetic algorithm with competition(MAC) is proposed to solve the capacitated green vehicle routing problem(CGVRP). Firstly, the permutation array called traveling salesman problem(TSP) route is used t... In this paper, a memetic algorithm with competition(MAC) is proposed to solve the capacitated green vehicle routing problem(CGVRP). Firstly, the permutation array called traveling salesman problem(TSP) route is used to encode the solution, and an effective decoding method to construct the CGVRP route is presented accordingly. Secondly, the k-nearest neighbor(k NN) based initialization is presented to take use of the location information of the customers. Thirdly, according to the characteristics of the CGVRP, the search operators in the variable neighborhood search(VNS) framework and the simulated annealing(SA) strategy are executed on the TSP route for all solutions. Moreover, the customer adjustment operator and the alternative fuel station(AFS) adjustment operator on the CGVRP route are executed for the elite solutions after competition. In addition, the crossover operator is employed to share information among different solutions. The effect of parameter setting is investigated using the Taguchi method of design-ofexperiment to suggest suitable values. Via numerical tests, it demonstrates the effectiveness of both the competitive search and the decoding method. Moreover, extensive comparative results show that the proposed algorithm is more effective and efficient than the existing methods in solving the CGVRP. 展开更多
关键词 Capacitated green vehicle routing problem(CGVRP) competition k-nearest neighbor(kNN) local intensification memetic algorithm
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Models for Location Inventory Routing Problem of Cold Chain Logistics with NSGA-Ⅱ Algorithm 认领 引用 被引量：3
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作者 郑建国 李康 伍大清 《Journal of Donghua University(English Edition)》 CAS 2017年第4期533-539,共7页
In this paper,a novel location inventory routing(LIR)model is proposed to solve cold chain logistics network problem under uncertain demand environment. The goal of the developed model is to optimize costs of location... In this paper,a novel location inventory routing(LIR)model is proposed to solve cold chain logistics network problem under uncertain demand environment. The goal of the developed model is to optimize costs of location,inventory and transportation.Due to the complex of LIR problem( LIRP), a multi-objective genetic algorithm(GA), non-dominated sorting in genetic algorithm Ⅱ( NSGA-Ⅱ) has been introduced. Its performance is tested over a real case for the proposed problems. Results indicate that NSGA-Ⅱ provides a competitive performance than GA,which demonstrates that the proposed model and multi-objective GA are considerably efficient to solve the problem. 展开更多
关键词 cold chain logistics multi-objective location inventory routing problem(LIRP) non-dominated sorting in genetic algorithm Ⅱ(NSGA-Ⅱ)
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