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Effective data transmission through energy-efficient clustering and Fuzzy-Based IDS routing approach in WSNs
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作者 Saziya TABBASSUM Rajesh Kumar PATHAK 《虚拟现实与智能硬件(中英文)》 EI 2024年第1期1-16,共16页
Wireless sensor networks(WSN)gather information and sense information samples in a certain region and communicate these readings to a base station(BS).Energy efficiency is considered a major design issue in the WSNs,a... Wireless sensor networks(WSN)gather information and sense information samples in a certain region and communicate these readings to a base station(BS).Energy efficiency is considered a major design issue in the WSNs,and can be addressed using clustering and routing techniques.Information is sent from the source to the BS via routing procedures.However,these routing protocols must ensure that packets are delivered securely,guaranteeing that neither adversaries nor unauthentic individuals have access to the sent information.Secure data transfer is intended to protect the data from illegal access,damage,or disruption.Thus,in the proposed model,secure data transmission is developed in an energy-effective manner.A low-energy adaptive clustering hierarchy(LEACH)is developed to efficiently transfer the data.For the intrusion detection systems(IDS),Fuzzy logic and artificial neural networks(ANNs)are proposed.Initially,the nodes were randomly placed in the network and initialized to gather information.To ensure fair energy dissipation between the nodes,LEACH randomly chooses cluster heads(CHs)and allocates this role to the various nodes based on a round-robin management mechanism.The intrusion-detection procedure was then utilized to determine whether intruders were present in the network.Within the WSN,a Fuzzy interference rule was utilized to distinguish the malicious nodes from legal nodes.Subsequently,an ANN was employed to distinguish the harmful nodes from suspicious nodes.The effectiveness of the proposed approach was validated using metrics that attained 97%accuracy,97%specificity,and 97%sensitivity of 95%.Thus,it was proved that the LEACH and Fuzzy-based IDS approaches are the best choices for securing data transmission in an energy-efficient manner. 展开更多
关键词 Low energy adaptive clustering hierarchy(LEACH) Intrusion detection system(IDS) Wireless sensor network(WSN) Fuzzy logic and artificial neural network(ANN)
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Research on Adaptive Cluster Sampling Method Based on PPS
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作者 Shaohua Wang Ting Yang 《Journal of Applied Mathematics and Physics》 2024年第5期1668-1681,共14页
This paper introduces the principle of PPS-based adaptive cluster sampling method and its modified HH estimator and HT estimator calculation method. It compares PPS-based adaptive cluster sampling method with SRS samp... This paper introduces the principle of PPS-based adaptive cluster sampling method and its modified HH estimator and HT estimator calculation method. It compares PPS-based adaptive cluster sampling method with SRS sampling and SRS-based adaptive group. The difference between the group sampling and the advantages and scope of the PPS adaptive cluster sampling method are analyzed. According to the case analysis, the relevant conclusions are drawn: 1) The adaptive cluster sampling method is more accurate than the SRS sampling;2) SRS adaptive The HT estimator of the cluster sampling is more stable than the HH estimator;3) The two estimators of the PPS adaptive cluster sampling method have little difference in the estimation of the population mean, but the HT estimator variance is smaller and more suitable;4) PPS The HH estimator of adaptive cluster sampling is the same as the HH estimator of SRS adaptive cluster sampling, but the variance is larger and unstable. 展开更多
关键词 PPS adaptive Cluster Sampling Modified HH Estimation Modified HT Estimation
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Design of Clustering Techniques in Cognitive Radio Sensor Networks
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作者 R.Ganesh Babu D.Hemanand +1 位作者 V.Amudha S.Sugumaran 《Computer Systems Science & Engineering》 SCIE EI 2023年第1期441-456,共16页
In recent decades,several optimization algorithms have been developed for selecting the most energy efficient clusters in order to save power during trans-mission to a shorter distance while restricting the Primary Us... In recent decades,several optimization algorithms have been developed for selecting the most energy efficient clusters in order to save power during trans-mission to a shorter distance while restricting the Primary Users(PUs)interfer-ence.The Cognitive Radio(CR)system is based on the Adaptive Swarm Distributed Intelligent based Clustering algorithm(ASDIC)that shows better spectrum sensing among group of multiusers in terms of sensing error,power sav-ing,and convergence time.In this research paper,the proposed ASDIC algorithm develops better energy efficient distributed cluster based sensing with the optimal number of clusters on their connectivity.In this research,multiple random Sec-ondary Users(SUs),and PUs are considered for implementation.Hence,the pro-posed ASDIC algorithm improved the convergence speed by combining the multi-users clustered communication compared to the existing optimization algo-rithms.Experimental results showed that the proposed ASDIC algorithm reduced the node power of 9.646%compared to the existing algorithms.Similarly,ASDIC algorithm reduced 24.23%of SUs average node power compared to the existing algorithms.Probability of detection is higher by reducing the Signal-to-Noise Ratio(SNR)to 2 dB values.The proposed ASDIC delivers low false alarm rate compared to other existing optimization algorithms in the primary detection.Simulation results showed that the proposed ASDIC algorithm effectively solves the multimodal optimization problems and maximizes the performance of net-work capacity. 展开更多
关键词 adaptive swarm distributed clustering cognitive radio clustering algorithm distributed swarm intelligent energy efficient distributed cluster-based sensing multi modal optimization
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Accelerated Particle Swarm Optimization Algorithm for Efficient Cluster Head Selection in WSN
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作者 Imtiaz Ahmad Tariq Hussain +3 位作者 Babar Shah Altaf Hussain Iqtidar Ali Farman Ali 《Computers, Materials & Continua》 SCIE EI 2024年第6期3585-3629,共45页
Numerous wireless networks have emerged that can be used for short communication ranges where the infrastructure-based networks may fail because of their installation and cost.One of them is a sensor network with embe... Numerous wireless networks have emerged that can be used for short communication ranges where the infrastructure-based networks may fail because of their installation and cost.One of them is a sensor network with embedded sensors working as the primary nodes,termed Wireless Sensor Networks(WSNs),in which numerous sensors are connected to at least one Base Station(BS).These sensors gather information from the environment and transmit it to a BS or gathering location.WSNs have several challenges,including throughput,energy usage,and network lifetime concerns.Different strategies have been applied to get over these restrictions.Clustering may,therefore,be thought of as the best way to solve such issues.Consequently,it is crucial to analyze effective Cluster Head(CH)selection to maximize efficiency throughput,extend the network lifetime,and minimize energy consumption.This paper proposed an Accelerated Particle Swarm Optimization(APSO)algorithm based on the Low Energy Adaptive Clustering Hierarchy(LEACH),Neighboring Based Energy Efficient Routing(NBEER),Cooperative Energy Efficient Routing(CEER),and Cooperative Relay Neighboring Based Energy Efficient Routing(CR-NBEER)techniques.With the help of APSO in the implementation of the WSN,the main methodology of this article has taken place.The simulation findings in this study demonstrated that the suggested approach uses less energy,with respective energy consumption ranges of 0.1441 to 0.013 for 5 CH,1.003 to 0.0521 for 10 CH,and 0.1734 to 0.0911 for 15 CH.The sending packets ratio was also raised for all three CH selection scenarios,increasing from 659 to 1730.The number of dead nodes likewise dropped for the given combination,falling between 71 and 66.The network lifetime was deemed to have risen based on the results found.A hybrid with a few valuable parameters can further improve the suggested APSO-based protocol.Similar to underwater,WSN can make use of the proposed protocol.The overall results have been evaluated and compared with the existing approaches of sensor networks. 展开更多
关键词 Wireless sensor network cluster head selection low energy adaptive clustering hierarchy accelerated particle swarm optimization
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Two-Level Linear Clustering Protocol Based on Wireless Sensor Networks 被引量:1
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作者 Mei Hu Yong-Xi Wang 《Journal of Electronic Science and Technology》 CAS CSCD 2016年第3期257-261,共5页
Aiming at the defects of the nodes in the low energy adaptive clustering hierarchy (LEACH) protocol, such as high energy consumption and uneven energy consumption, a two-level linear clustering protocol is built. Th... Aiming at the defects of the nodes in the low energy adaptive clustering hierarchy (LEACH) protocol, such as high energy consumption and uneven energy consumption, a two-level linear clustering protocol is built. The protocol improves the way of the nodes distribution at random. The terminal nodes which have not been a two-level cluster head in the cluster can compete with the principle of equivalent possibility, and on the basis of the rest energy of nodes the two-level cluster head is selected at last. The single hop within the cluster and single hop or multiple hops between clusters are used. Simulation experiment results show that the performance of the two-level linear clustering protocol applied to the Hexi corridor agricultural field is superior to that of the LEACH protocol in the survival time of network nodes, the ratio of success, and the remaining energy of network nodes. 展开更多
关键词 clustering ENERGY-EFFICIENT FARMLAND Hexi corridor low energy adaptive clustering hierarchy (LEACH) LINEAR wireless sensor network (WSN).
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A Distributed Clustering Algorithm for Wireless Sensor Networks
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作者 SHANG Fengjun 《Wuhan University Journal of Natural Sciences》 CAS 2008年第4期385-390,共6页
In the paper, we consider a network of energy constrained sensors deployed over a region. Each sensor node in such a network is systematically gathering and transmitting sensed data to a base station (via clusterhead... In the paper, we consider a network of energy constrained sensors deployed over a region. Each sensor node in such a network is systematically gathering and transmitting sensed data to a base station (via clusterhead) for further processing. The key problem focuses on how to reduce the power consumption of wireless microsensor networks. The core includes the energy efficiency of clusterheads and that of cluster members. We first extend low-energy adaptive clustering hierarchy (LEACH)'s stochastic clusterhead selection algorithm by a factor with distance-based deterministic component (LEACH-D) to reduce energy consumption for energy efficiency of clusterhead. And the cost function is proposed so that it balances the energy consumption of nodes for energy efficiency of cluster member. Simulation results show that our modified scheme can extend the network life around up to 40% before first node dies. Through both theoretical analysis and numerical results, it is shown that the proposed algorithm achieves better performance than the existing representative methods. 展开更多
关键词 data aggregation wireless microsensor networks low-energy adaptive clustering hierarchy (LEACH) clusterhead selection
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Fuzzy Service Aware Adaptive Cooperative Spectrum Sensing Algorithm
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作者 Gongan Qiu Shibing Zhang Xiaoge Zhang 《China Communications》 SCIE CSCD 2016年第12期250-260,共11页
Based on the service characteristics and the sensing ability for secondary users, a joint optimization scheme of spectrum detection and allocation is investigated to expand the available sensing region and allocate th... Based on the service characteristics and the sensing ability for secondary users, a joint optimization scheme of spectrum detection and allocation is investigated to expand the available sensing region and allocate the Qo S-specified channels. On the aspect of spectrum detection, due to the available detection index with the global detection metrics, cooperation thresholds are adaptively adjusted to select the cooperative model for maximizing the available sensing region. On the aspect of spectrum allocation, for different service category, the idle channels are efficiently allocated that depend on their stability and available bandwidth. Meanwhile, based on the requested rates defined by fuzzy theory, the secondary users can be divided into two categories, i.e.,delay sensitive service and reliability sensitive service. Finally, the Qo S-specified channels from the targeted spectrum subset are allocated to secondary users. Simulation results show that our proposed algorithm can not only expand the available sensing region,but also decrease the outage probability of delay sensitive services. Additionally, it enables stable power consumption in the time-variation channel. 展开更多
关键词 spectrum detection spectrum allocation adaptive cluster cooperation fuzzy service awareness
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Cluster Head Selection and Multipath Routing Based Energy Efficient Wireless Sensor Network
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作者 T.Shanmugapriya Dr.K.Kousalya 《Intelligent Automation & Soft Computing》 SCIE 2023年第4期879-894,共16页
The Wireless Sensor Network(WSN)is a network of Sensor Nodes(SN)which adopt radio signals for communication amongst themselves.There is an increase in the prominence of WSN adaptability to emerging applications like t... The Wireless Sensor Network(WSN)is a network of Sensor Nodes(SN)which adopt radio signals for communication amongst themselves.There is an increase in the prominence of WSN adaptability to emerging applications like the Internet of Things(IoT)and Cyber-Physical Systems(CPS).Data secur-ity,detection of faults,management of energy,collection and distribution of data,network protocol,network coverage,mobility of nodes,and network heterogene-ity are some of the issues confronted by WSNs.There is not much published information on issues related to node mobility and management of energy at the time of aggregation of data.Towards the goal of boosting the mobility-based WSNs’network performance and energy,data aggregation protocols such as the presently-used Mobility Low-Energy Adaptive Clustering Hierarchy(LEACH-M)and Energy Efficient Heterogeneous Clustered(EEHC)scheme have been exam-ined in this work.A novel Artificial Bee Colony(ABC)algorithm is proposed in this work for effective election of CHs and multipath routing in WSNs so as to enable effective data transfer to the Base Station(BS)with least energy utilization.There is avoidance of the local optima problem at the time of solution space search in this proposed technique.Experimentations have been conducted on a large WSN network that has issues with mobility of nodes. 展开更多
关键词 Wireless sensor network ROUTING clustering MOBILITY low-energy adaptive clustering hierarchy energy efficient heterogeneous clustered artificial bee colony
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An Optimized Novel Trust-Based Security Mechanism Using Elephant Herd Optimization
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作者 Saranya Veerapaulraj M.Karthikeyan +1 位作者 S.Sasipriya A.S.Shanthi 《Computer Systems Science & Engineering》 SCIE EI 2023年第3期2489-2500,共12页
Routing strategies and security issues are the greatest challenges in Wireless Sensor Network(WSN).Cluster-based routing Low Energy adaptive Clustering Hierarchy(LEACH)decreases power consumption and increases net-wor... Routing strategies and security issues are the greatest challenges in Wireless Sensor Network(WSN).Cluster-based routing Low Energy adaptive Clustering Hierarchy(LEACH)decreases power consumption and increases net-work lifetime considerably.Securing WSN is a challenging issue faced by researchers.Trust systems are very helpful in detecting interfering nodes in WSN.Researchers have successfully applied Nature-inspired Metaheuristics Optimization Algorithms as a decision-making factor to derive an improved and effective solution for a real-time optimization problem.The metaheuristic Elephant Herding Optimizations(EHO)algorithm is formulated based on ele-phant herding in their clans.EHO considers two herding behaviors to solve and enhance optimization problem.Based on Elephant Herd Optimization,a trust-based security method is built in this work.The proposed routing selects routes to destination based on the trust values,thus,finding optimal secure routes for transmitting data.Experimental results have demonstrated the effectiveness of the proposed EHO based routing.The Average Packet Loss Rate of the proposed Trust Elephant Herd Optimization performs better by 35.42%,by 1.45%,and by 31.94%than LEACH,Elephant Herd Optimization,and Trust LEACH,respec-tively at Number of Nodes 3000.As the proposed routing is efficient in selecting secure routes,the average packet loss rate is significantly reduced,improving the network’s performance.It is also observed that the lifetime of the network is enhanced with the proposed Trust Elephant Herd Optimization. 展开更多
关键词 Wireless sensor network low energy adaptive clustering hierarchy trust systems elephant herding optimizations algorithm trust-based elephant herd optimization
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An Energy-Efficient Multi-swarm Optimization in Wireless Sensor Networks
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作者 Reem Alkanhel Kalaiselvi Chinnathambi +4 位作者 C.Thilagavathi Mohamed Abouhawwash Mona A.Al duailij Manal Abdullah Alohali Doaa Sami Khafaga 《Intelligent Automation & Soft Computing》 SCIE 2023年第5期1571-1583,共13页
Wireless Sensor Networks are a group of sensors with inadequate power sources that are installed in a particular region to gather information from the surroundings.Designing energy-efficient data gathering methods in l... Wireless Sensor Networks are a group of sensors with inadequate power sources that are installed in a particular region to gather information from the surroundings.Designing energy-efficient data gathering methods in large-scale Wireless Sensor Networks(WSN)is one of the most difficult areas of study.As every sensor node has afinite amount of energy.Battery power is the most significant source in the WSN.Clustering is a well-known technique for enhan-cing the power feature in WSN.In the proposed method multi-Swarm optimiza-tion based on a Genetic Algorithm and Adaptive Hierarchical clustering-based routing protocol are used for enhancing the network’s lifespan and routing opti-mization.By using distributed data transmission modification,an adaptive hier-archical clustering-based routing algorithm for power consumption is presented to ensure continuous coverage of the entire area.To begin,a hierarchical cluster-ing-based routing protocol is presented in terms of balancing node energy con-sumption.The Multi-Swarm optimization(MSO)based Genetic Algorithms are proposed to select an efficient Cluster Head(CH).It also improves the network’s longevity and optimizes the routing.As a result of the study’sfindings,the pro-posed MSO-Genetic Algorithm with Hill climbing(GAHC)is effective,as it increases the number of clusters created,average energy expended,lifespan com-putation reduces average packet loss,and end-to-end delay. 展开更多
关键词 clustering energy consumption genetic algorithm multi swarm optimization adaptive hierarchical clustering ROUTING cluster head
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Passive location estimation using scatterer information for non-line-of-sight environments 被引量:1
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作者 颜俊 王林汝 吴乐南 《Journal of Southeast University(English Edition)》 EI CAS 2010年第4期518-522,共5页
In order to improve the performance of the traditional hybrid time-of-arrival(TOA)/angle-of-arrival(AOA)location algorithm in non-line-of-sight(NLOS)environments,a new hybrid TOA/AOA location estimation algorith... In order to improve the performance of the traditional hybrid time-of-arrival(TOA)/angle-of-arrival(AOA)location algorithm in non-line-of-sight(NLOS)environments,a new hybrid TOA/AOA location estimation algorithm by utilizing scatterer information is proposed.The linearized region of the mobile station(MS)is obtained according to the base station(BS)coordinates and the TOA measurements.The candidate points(CPs)of the MS are generated from this region.Then,using the measured TOA and AOA measurements,the radius of each scatterer is computed.Compared with the prior scatterer information,true CPs are obtained among all the CPs.The adaptive fuzzy clustering(AFC)technology is adopted to estimate the position of the MS with true CPs.Finally,simulations are conducted to evaluate the performance of the algorithm.The results demonstrate that the proposed location algorithm can significantly mitigate the NLOS effect and efficiently estimate the MS position. 展开更多
关键词 passive location time-of-arrival/angle-of-arrival(TOA/AOA) non-line-of-sight(NLOS)mitigation adaptive fuzzy clustering
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An Efficient Key Management Protocol for Wireless Sensor Networks 被引量:1
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作者 ZENG Qingguang CUI Yanling LUO Juan 《Wuhan University Journal of Natural Sciences》 CAS 2006年第6期1927-1930,共4页
Key management is a fundamental security service in wireless sensor networks. The communication security problems for these networks are exacerbated by the limited power and energy of the sensor devices. In this paper... Key management is a fundamental security service in wireless sensor networks. The communication security problems for these networks are exacerbated by the limited power and energy of the sensor devices. In this paper, we describe the design and implementation of an efficient key management scheme based on low energy adaptive clustering hierarchy(LEACH) for wireless sensor networks. The design of the protocol is motivated by the observation that many sensor nodes in the network play different roles. The paper presents different keys are set to the sensors for meeting different transmitting messages and variable security requirements. Simulation results show that our key management protocol based-on LEACH can achieve better performance. The energy consumption overhead introduced is remarkably low compared with the original Kerberos schemes. 展开更多
关键词 sensor networks key management SECURITY low energy adaptive clustering hierarchy (LEACH)
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Cross-classes domain inference with network sampling for natural resource inventory 被引量:1
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作者 Zhengyang Hou Ronald E.McRoberts +5 位作者 Chunyu Zhang Göran Ståhl Xiuhai Zhao Xuejun Wang Bo Li Qing Xu 《Forest Ecosystems》 SCIE CSCD 2022年第3期311-322,共12页
There are two distinct types of domains,design-and cross-classes domains,with the former extensively studied under the topic of small-area estimation.In natural resource inventory,however,most classes listed in the co... There are two distinct types of domains,design-and cross-classes domains,with the former extensively studied under the topic of small-area estimation.In natural resource inventory,however,most classes listed in the condition tables of national inventory programs are characterized as cross-classes domains,such as vegetation type,productivity class,and age class.To date,challenges remain active for inventorying cross-classes domains because these domains are usually of unknown sampling frame and spatial distribution with the result that inference relies on population-level as opposed to domain-level sampling.Multiple challenges are noteworthy:(1)efficient sampling strategies are difficult to develop because of little priori information about the target domain;(2)domain inference relies on a sample designed for the population,so within-domain sample sizes could be too small to support a precise estimation;and(3)increasing sample size for the population does not ensure an increase to the domain,so actual sample size for a target domain remains highly uncertain,particularly for small domains.In this paper,we introduce a design-based generalized systematic adaptive cluster sampling(GSACS)for inventorying cross-classes domains.Design-unbiased Hansen-Hurwitz and Horvitz-Thompson estimators are derived for domain totals and compared within GSACS and with systematic sampling(SYS).Comprehensive Monte Carlo simulations show that(1)GSACS Hansen-Hurwitz and Horvitz-Thompson estimators are unbiased and equally efficient,whereas thelatter outperforms the former for supporting a sample of size one;(2)SYS is a special case of GSACS while the latter outperforms the former in terms of increased efficiency and reduced intensity;(3)GSACS Horvitz-Thompson variance estimator is design-unbiased for a single SYS sample;and(4)rules-ofthumb summarized with respect to sampling design and spatial effect improve precision.Because inventorying a mini domain is analogous to inventorying a rare variable,alternative network sampling procedures are also readily available for inventorying cross-classes domains. 展开更多
关键词 Cross-classes domain estimation Design-based inference Network sampling Generalized systematic adaptive cluster sampling Forest inventory
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Two-Stage Negative Adaptive Cluster Sampling
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作者 R.V.Latpate J.K.Kshirsagar 《Communications in Mathematics and Statistics》 SCIE 2020年第1期1-21,共21页
If the population is rare and clustered,then simple random sampling gives a poor estimate of the population total.For such type of populations,adaptive cluster sampling is useful.But it loses control on the final samp... If the population is rare and clustered,then simple random sampling gives a poor estimate of the population total.For such type of populations,adaptive cluster sampling is useful.But it loses control on the final sample size.Hence,the cost of sampling increases substantially.To overcome this problem,the surveyors often use auxiliary information which is easy to obtain and inexpensive.An attempt is made through the auxiliary information to control the final sample size.In this article,we have proposed two-stage negative adaptive cluster sampling design.It is a new design,which is a combination of two-stage sampling and negative adaptive cluster sampling designs.In this design,we consider an auxiliary variablewhich is highly negatively correlatedwith the variable of interest and auxiliary information is completely known.In the first stage of this design,an initial random sample is drawn by using the auxiliary information.Further,using Thompson’s(JAmStat Assoc 85:1050-1059,1990)adaptive procedure networks in the population are discovered.These networks serve as the primary-stage units(PSUs).In the second stage,random samples of unequal sizes are drawn from the PSUs to get the secondary-stage units(SSUs).The values of the auxiliary variable and the variable of interest are recorded for these SSUs.Regression estimator is proposed to estimate the population total of the variable of interest.A new estimator,Composite Horwitz-Thompson(CHT)-type estimator,is also proposed.It is based on only the information on the variable of interest.Variances of the above two estimators along with their unbiased estimators are derived.Using this proposed methodology,sample survey was conducted at Western Ghat of Maharashtra,India.The comparison of the performance of these estimators and methodology is presented and compared with other existing methods.The cost-benefit analysis is given. 展开更多
关键词 adaptive cluster sampling Two-stage cluster sampling Negative adaptive cluster sampling Two-stage NACS Regression estimator
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A Necessary Condition about the Optimum Partition on a Finite Set of Samples and Its Application to Clustering Analysis
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作者 叶世伟 史忠植 《Journal of Computer Science & Technology》 SCIE EI CSCD 1995年第6期545-556,共12页
This paper presents another necessary condition about the optimum parti-tion on a finite set of samples. From this condition, a corresponding generalized sequential hao f k-means (GSHKM) clustering algorithm is built ... This paper presents another necessary condition about the optimum parti-tion on a finite set of samples. From this condition, a corresponding generalized sequential hao f k-means (GSHKM) clustering algorithm is built and many well-known clustering algorithms are found to be included in it. Under some assumptions the well-known MacQueen's SHKM (Sequential Hard K-Means)algorithm, FSCL (Frequency Sensitive Competitive Learning) algorithm and RPCL (Rival Penalized Competitive Learning) algorithm are derived. It is shown that FSCL in fact still belongs to the kind of GSHKM clustering algth rithm and is more suitable for producing means of K-partition of sample data,which is illustrated by numerical experiment. Meanwhile, some improvements on these algorithms are also given. 展开更多
关键词 Cluster analysis MacQueen's sequential hard K-means clustering algorithm frequency sensitive competitive learning adaptive frequency K-means clustering
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Combining Clustered Adaptive Multistart and Discrete Dynamic Convexized Method for the Max-Cut Problem
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作者 Geng Lin Wenxing Zhu 《Journal of the Operations Research Society of China》 EI 2014年第2期237-262,共26页
Given an undirected graph with edge weights,the max-cut problem is to find a partition of the vertices into twosubsets,such that the sumof theweights of the edges crossing different subsets ismaximized.Heuristics base... Given an undirected graph with edge weights,the max-cut problem is to find a partition of the vertices into twosubsets,such that the sumof theweights of the edges crossing different subsets ismaximized.Heuristics based on auxiliary function can obtain high-quality solutions of the max-cut problem,but suffer high solution cost when instances grow large.In this paper,we combine clustered adaptive multistart and discrete dynamic convexized method to obtain high-quality solutions in a reasonable time.Computational experiments on two sets of benchmark instances from the literature were performed.Numerical results and comparisons with some heuristics based on auxiliary function show that the proposed algorithm is much faster and can obtain better solutions.Comparisons with several state-ofthe-science heuristics demonstrate that the proposed algorithm is competitive. 展开更多
关键词 MAX-CUT Local search Dynamic convexized method Clustered adaptive multistart
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k-step adaptive cluster sampling with Horvitz-Thompson estimator
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作者 Quangyu Zhu Liyong Fu 《International Journal of Biomathematics》 SCIE 2018年第2期299-317,共19页
Adaptive cluster sampling (ACS) has been widely used for data collection of environment and natural resources. However, the randomness of its final sample size often impedes the use of this method. To control the fi... Adaptive cluster sampling (ACS) has been widely used for data collection of environment and natural resources. However, the randomness of its final sample size often impedes the use of this method. To control the final sample sizes, in this study, a k-step ACS based on Horvitz-Thompson (HT) estimator was developed and an unbiased estimator was derived. The k-step ACS-HT was assessed first using a simulated example and then using a real survey for numbers of plants for three species that were characterized by clustered and patchily spatial distributions. The effectiveness of this sampling design method was assessed in comparison with ACS Hansen-Hurwitz (ACS-HH) and ACS- HT estimators, and k-step ACS-HT estimator. The effectiveness of using different k- step sizes was also compared. The results showed that k-step ACS^HT estimator was most effective and ACS-HH was the least. Moreover, stable sample mean and variance estimates could be obtained after a certain number of steps, but depending on plant species, k-step ACS without replacement was slightly more effective than that with replacement. In k-step ACS, the variance estimate of one-step ACS is much larger than other k-step ACS (k 〉 1), but it is smaller than ACS. This implies that k-step ACS is more effective than traditional ACS, besides, the final sample size can be controlled easily in population with big clusters. 展开更多
关键词 Finalized sample size k-step adaptive cluster sampling plant species sample design inlprovement unbiased estimator.
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