The uncertain nature of mapping user tasks to Virtual Machines(VMs) causes system failure or execution delay in Cloud Computing.To maximize cloud resource throughput and decrease user response time,load balancing is n...The uncertain nature of mapping user tasks to Virtual Machines(VMs) causes system failure or execution delay in Cloud Computing.To maximize cloud resource throughput and decrease user response time,load balancing is needed.Possible load balancing is needed to overcome user task execution delay and system failure.Most swarm intelligent dynamic load balancing solutions that used hybrid metaheuristic algorithms failed to balance exploitation and exploration.Most load balancing methods were insufficient to handle the growing uncertainty in job distribution to VMs.Thus,the Hybrid Spotted Hyena and Whale Optimization Algorithm-based Dynamic Load Balancing Mechanism(HSHWOA) partitions traffic among numerous VMs or servers to guarantee user chores are completed quickly.This load balancing approach improved performance by considering average network latency,dependability,and throughput.This hybridization of SHOA and WOA aims to improve the trade-off between exploration and exploitation,assign jobs to VMs with more solution diversity,and prevent the solution from reaching a local optimality.Pysim-based experimental verification and testing for the proposed HSHWOA showed a 12.38% improvement in minimized makespan,16.21% increase in mean throughput,and 14.84% increase in network stability compared to baseline load balancing strategies like Fractional Improved Whale Social Optimization Based VM Migration Strategy FIWSOA,HDWOA,and Binary Bird Swap.展开更多
Due to the NP-hardness of the two-sided assembly line balancing （TALB） problem, multiple constraints existing in real applications are less studied, especially when one task is involved with several constraints. In ...Due to the NP-hardness of the two-sided assembly line balancing （TALB） problem, multiple constraints existing in real applications are less studied, especially when one task is involved with several constraints. In this paper, an effective hybrid algorithm is proposed to address the TALB problem with multiple constraints （TALB-MC）. Considering the discrete attribute of TALB-MC and the continuous attribute of the standard teaching-learning-based optimization （TLBO） algorithm, the random-keys method is hired in task permutation representation, for the purpose of bridging the gap between them. Subsequently, a special mechanism for handling multiple constraints is developed. In the mechanism, the directions constraint of each task is ensured by the direction check and adjustment. The zoning constraints and the synchronism constraints are satisfied by teasing out the hidden correlations among constraints. The positional constraint is allowed to be violated to some extent in decoding and punished in cost fimction. Finally, with the TLBO seeking for the global optimum, the variable neighborhood search （VNS） is further hybridized to extend the local search space. The experimental results show that the proposed hybrid algorithm outperforms the late acceptance hill-climbing algorithm （LAHC） for TALB-MC in most cases, especially for large-size problems with multiple constraints, and demonstrates well balance between the exploration and the exploitation. This research proposes an effective and efficient algorithm for solving TALB-MC problem by hybridizing the TLBO and VNS.展开更多
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
One of the challenging scheduling problems in Cloud data centers is to take the allocation and migration of reconfigurable virtual machines as well as the integrated features of hosting physical machines into consider...One of the challenging scheduling problems in Cloud data centers is to take the allocation and migration of reconfigurable virtual machines as well as the integrated features of hosting physical machines into consideration. We introduce a Dynamic and Integrated Resource Scheduling algorithm (DAIRS) for Cloud data centers. Unlike traditional load-balance scheduling algorithms which often consider only one factor such as the CPU load in physical servers, DAIRS treats CPU, memory and network bandwidth integrated for both physical machines and virtual machines. We develop integrated measurement for the total imbalance level of a Cloud datacenter as well as the average imbalance level of each server. Simulation results show that DAIRS has good performance with regard to total imbalance level, average imbalance level of each server, as well as overall running time.展开更多
In a manufacturing industry, mixed model assembly line(MMAL) is preferred in order to meet the variety in product demand. MMAL balancing helps in assembling products with similar characteristics in a random fashion. T...In a manufacturing industry, mixed model assembly line(MMAL) is preferred in order to meet the variety in product demand. MMAL balancing helps in assembling products with similar characteristics in a random fashion. The objective of this work aims in reducing the number of workstations, work load index between stations and within each station. As manual contribution of workers in final assembly line is more, ergonomics is taken as an additional objective function. Ergonomic risk level of a workstation is evaluated using a parameter called accumulated risk posture(ARP), which is calculated using rapid upper limb assessment(RULA) check sheet. This work is based on the case study of an MMAL problem in Rane(Madras) Ltd.(India), in which a problem based genetic algorithm(GA) has been proposed to minimize the mentioned objectives. The working of the genetic operators such as selection, crossover and mutation has been modified with respect to the addressed MMAL problem. The results show that there is a significant impact over productivity and the process time of the final assembled product, i.e., the rate of production is increased by 39.5% and the assembly time for one particular model is reduced to 13 min from existing 18 min. Also, the space required using the proposed assembly line is only 200 m2 against existing 350 m2. Further, the algorithm helps in reducing workers fatigue(i.e., ergonomic friendly).展开更多
The growing global competition compels manufacturing organizations to engage themselves in all productivity improvement activities. In this direction, the consideration of mixed-model assembly line balancing problem a...The growing global competition compels manufacturing organizations to engage themselves in all productivity improvement activities. In this direction, the consideration of mixed-model assembly line balancing problem and implementing in industries plays a major role in improving organizational productivity. In this paper, the mixed model assembly line balancing problem with deterministic task times is considered. The authors made an attempt to develop a genetic algorithm for realistic design of the mixed-model assembly line balancing problem. The design is made using the originnal task times of the models, which is a realistic approach. Then, it is compared with the generally perceived design of the mixed-model assembly line balancing problem.展开更多
Task scheduling in highly elastic and dynamic processing environments such as cloud computing have become the most discussed problem among researchers.Task scheduling algorithms are responsible for the allocation of t...Task scheduling in highly elastic and dynamic processing environments such as cloud computing have become the most discussed problem among researchers.Task scheduling algorithms are responsible for the allocation of the tasks among the computing resources for their execution,and an inefficient task scheduling algorithm results in under-or over-utilization of the resources,which in turn leads to degradation of the services.Therefore,in the proposed work,load balancing is considered as an important criterion for task scheduling in a cloud computing environment as it can help in reducing the overhead in the critical decision-oriented process.In this paper,we propose an adaptive genetic algorithm-based load balancing(GALB)-aware task scheduling technique that not only results in better utilization of resources but also helps in optimizing the values of key performance indicators such as makespan,performance improvement ratio,and degree of imbalance.The concept of adaptive crossover and mutation is used in this work which results in better adaptation for the fittest individual of the current generation and prevents them from the elimination.CloudSim simulator has been used to carry out the simulations and obtained results establish that the proposed GALB algorithm performs better for all the key indicators and outperforms its peers which are taken into the consideration.展开更多
Because of different system capacities of base station （BS） or access point （AP） and ununiformity of traffic distribution in different cells, quantities of new call users may be blocked in overloaded cell in commu...Because of different system capacities of base station （BS） or access point （AP） and ununiformity of traffic distribution in different cells, quantities of new call users may be blocked in overloaded cell in communication hot spots. Whereas in some neighboring under-loaded cells, bandwidth may be superfluous because there are only few users to request services. In order to raise resource utilization of the whole heterogeneous networks, several novel load balancing strategies are proposed, which combine the call ad- mission control policy and multi-hop routing protocol of ad-hoc network for load balancing. These loadbalancing strategies firstly make a decision whether to admit a new call or not by considering some parameters like load index and route cost, etc., and then transfer the denied users into neighboring under-loaded cell with surplus channel according to optimum multi-hop routing algorithm. Simulation results show that the proposed load balancing strategies can distribute traffics to the whole heterogeneous wireless netorks, improve the load balance index efficiently, and avoid the call block phenomenon almost absolutely.展开更多
Aiming at assembly line balancing problem,a double chromosome genetic algorithm(DCGA)is proposed to avoid trapping in local optimum,which is a disadvantage of standard genetic algorithm(SGA).In this algorithm,there ar...Aiming at assembly line balancing problem,a double chromosome genetic algorithm(DCGA)is proposed to avoid trapping in local optimum,which is a disadvantage of standard genetic algorithm(SGA).In this algorithm,there are two chromosomes of each individual,and the better one,regarded as dominant chromosome,determines the fitness.Dominant chromosome keeps excellent gene segments to speed up the convergence,and recessive chromosome maintains population diversity to get better global search ability to avoid local optimal solution.When the amounts of chromosomes are equal,the population size of DCGA is half that of SGA,which significantly reduces evolutionary time.Finally,the effectiveness is verified by experiments.展开更多
Recently,the fundamental problem with Hybrid Mobile Ad-hoc Net-works(H-MANETs)is tofind a suitable and secure way of balancing the load through Internet gateways.Moreover,the selection of the gateway and overload of th...Recently,the fundamental problem with Hybrid Mobile Ad-hoc Net-works(H-MANETs)is tofind a suitable and secure way of balancing the load through Internet gateways.Moreover,the selection of the gateway and overload of the network results in packet loss and Delay(DL).For optimal performance,it is important to load balance between different gateways.As a result,a stable load balancing procedure is implemented,which selects gateways based on Fuzzy Logic(FL)and increases the efficiency of the network.In this case,since gate-ways are selected based on the number of nodes,the Energy Consumption(EC)was high.This paper presents a novel Node Quality-based Clustering Algo-rithm(NQCA)based on Fuzzy-Genetic for Cluster Head and Gateway Selection(FGCHGS).This algorithm combines NQCA with the Improved Weighted Clus-tering Algorithm(IWCA).The NQCA algorithm divides the network into clusters based upon node priority,transmission range,and neighbourfidelity.In addition,the simulation results tend to evaluate the performance effectiveness of the FFFCHGS algorithm in terms of EC,packet loss rate(PLR),etc.展开更多
This work investigates a multi-product parallel disassembly line balancing problem considering multi-skilled workers.A mathematical model for the parallel disassembly line is established to achieve maximized disassemb...This work investigates a multi-product parallel disassembly line balancing problem considering multi-skilled workers.A mathematical model for the parallel disassembly line is established to achieve maximized disassembly profit and minimized workstation cycle time.Based on a product’s AND/OR graph,matrices for task-skill,worker-skill,precedence relationships,and disassembly correlations are developed.A multi-objective discrete chemical reaction optimization algorithm is designed.To enhance solution diversity,improvements are made to four reactions:decomposition,synthesis,intermolecular ineffective collision,and wall invalid collision reaction,completing the evolution of molecular individuals.The established model and improved algorithm are applied to ball pen,flashlight,washing machine,and radio combinations,respectively.Introducing a Collaborative Resource Allocation(CRA)strategy based on a Decomposition-Based Multi-Objective Evolutionary Algorithm,the experimental results are compared with four classical algorithms:MOEA/D,MOEAD-CRA,Non-dominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ),and Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ).This validates the feasibility and superiority of the proposed algorithm in parallel disassembly production lines.展开更多
A balancing problem for a mixed model assembly line with uncertain task processmg Ume anO daily model mixed changes is considered, and the objective is to minimize the work variances between stations in the line. For ...A balancing problem for a mixed model assembly line with uncertain task processmg Ume anO daily model mixed changes is considered, and the objective is to minimize the work variances between stations in the line. For the balancing problem for the scenario-based robust assembly line with a finitely large number of potential scenarios, the direct solution methodology considering all potential scenarios is quite time-consuming. A scenario relaxation algorithm that embeds genetic al- gorithm is developed. This new algorithm guarantees termination at an optimal robust solution with relatively short running time, and makes it possible to solve robust problems with large quantities of potential scenarios. Extensive computational results are reported to show the efficiency and effectiveness of the proposed algorithm.展开更多
A comprehensive predictive strategy was proposed for the neutral-point balancing control of back-to-back three-level converters. The phase currents at both sides and the DC-link capacitor voltages were measured for th...A comprehensive predictive strategy was proposed for the neutral-point balancing control of back-to-back three-level converters. The phase currents at both sides and the DC-link capacitor voltages were measured for the prediction of the neutral-point current. A quality function was found to balance the neutral-point, and a metabolic on-times distribution factor was used as a predicator to minimize the quality function at each switching state. Simulation results show that the proposed method produces smaller ripples in tested signals compared with the established one, namely, 9.15% less in a total harmonic distortion(THD) of line-to-line voltage, 1.08% less in the THD of phase current, and 0.9 V less in the ripple of the neutral-point voltage. The obtained experimental results show that the main harmonics of the line-to-line voltage and the phase current in the proposed method are improved by 10 d B and 6 d B, respectively, and the ripple of neutral-point voltage is halved compared to the established one.展开更多
Cloud Computing has the ability to provide on-demand access to a shared resource pool.It has completely changed the way businesses are managed,implement applications,and provide services.The rise in popularity has led...Cloud Computing has the ability to provide on-demand access to a shared resource pool.It has completely changed the way businesses are managed,implement applications,and provide services.The rise in popularity has led to a significant increase in the user demand for services.However,in cloud environments efficient load balancing is essential to ensure optimal performance and resource utilization.This systematic review targets a detailed description of load balancing techniques including static and dynamic load balancing algorithms.Specifically,metaheuristic-based dynamic load balancing algorithms are identified as the optimal solution in case of increased traffic.In a cloud-based context,this paper describes load balancing measurements,including the benefits and drawbacks associated with the selected load balancing techniques.It also summarizes the algorithms based on implementation,time complexity,adaptability,associated issue(s),and targeted QoS parameters.Additionally,the analysis evaluates the tools and instruments utilized in each investigated study.Moreover,comparative analysis among static,traditional dynamic and metaheuristic algorithms based on response time by using the CloudSim simulation tool is also performed.Finally,the key open problems and potential directions for the state-of-the-art metaheuristic-based approaches are also addressed.展开更多
Influence coefficient method and the modal balancing method are often used in the dynamic balancing in the past days. These methods sometimes exist a lot of big measurement errors. So, in order to make these errors mu...Influence coefficient method and the modal balancing method are often used in the dynamic balancing in the past days. These methods sometimes exist a lot of big measurement errors. So, in order to make these errors much smaller, and to use the vibration information of the rotor more sufficiently, at last, we put forward the full vector dynamic balancing algorithm. Though the theoretical analysis, and the experiment tests, we can compare with the new method and the old method , study the relationship between the dynamic balancing and the rotation equipment, and the direction of the development. The full vector dynamic balancing algorithm theory can be inferred from the Jeffcott rotor. To compare with the methods which are mentioned before, we can find that the full vector dynamic balancing algorithm is much better than the influence coefficient method and the modal balancing method. We can use the MATLAB program to prove that the full vector dynamic balancing algorithm is much better. So the conclusion is completely right.展开更多
Aiming at the load imbalance and poor scalability in single-tier Web server clusters, an efficient load balancing ap- proach is proposed for constructing an N-hierarchical （multi-tier） Web server cluster. In each la...Aiming at the load imbalance and poor scalability in single-tier Web server clusters, an efficient load balancing ap- proach is proposed for constructing an N-hierarchical （multi-tier） Web server cluster. In each layer, multiple load balancers are set to receive the user requests simultaneously, and different load bal- ancing algorithms are used to construct the high-scalable Web cluster system. At the same time, an improved load balancing al- gorithm is proposed, which can dynamically calculate weights according to the utilization of the server resources, and reasonably distribute the loads for each server according to the load status of the servers. The experimental results show that the proposed ap- proach can greatly decrease the load imbalance among the Web servers and reduce the response time of the entire Web cluster system.展开更多
In this study,the Surface Energy Balance Algorithms for Land(SEBAL) model and Moderate Resolution Imaging Spectroradiometer(MODIS) products from Terra satellite were combined with meteorological data to estimate evapo...In this study,the Surface Energy Balance Algorithms for Land(SEBAL) model and Moderate Resolution Imaging Spectroradiometer(MODIS) products from Terra satellite were combined with meteorological data to estimate evapotranspiration(ET) over the Sanjiang Plain,Northeast China.Land cover/land use was classified by using a recursive partitioning and regression tree with MODIS Normalized Difference Vegetation Index(NDVI) time series data,which were reconstructed based on the Savitzky-Golay filtering approach.The MODIS product Quality Assessment Science Data Sets(QA-SDS) was analyzed and all scenes with valid data covering more than 75% of the Sanjiang Plain were selected for the SEBAL modeling.This provided 12 overpasses during 184-day growing season from May 1st to October 31st,2006.Daily ET estimated by the SEBAL model was misestimaed at the range of-11.29% to 27.57% compared with that measured by Eddy Covariance system(10.52% on average).The validation results show that seasonal ET from the SEBAL model is comparable to that from ground observation within 8.86% of deviation.Our results reveal that the time series daily ET of different land cover/use increases from vegetation on-going until June or July and then decreases as vegetation senesced.Seasonal ET is lower in dry farmland(average(Ave):491 mm) and paddy field(Ave:522 mm) and increases in wetlands to more than 586 mm.As expected,higher seasonal ET values are observed for the Xingkai Lake in the southeastern part of the Sanjiang Plain(Ave:823 mm),broadleaf forest(Ave:666 mm) and mixed wood(Ave:622 mm) in the southern/western Sanjiang Plain.The ET estimation with SEBAL using MODIS products can provide decision support for operational water management issues.展开更多
The topology of diode neutral-point-clamped(NPC)three-level inverter is prone to neutral-point potential offset.When the sum of three-phase current is zero,the virtual space vector pulse width modulation(VSVPWM)scheme...The topology of diode neutral-point-clamped(NPC)three-level inverter is prone to neutral-point potential offset.When the sum of three-phase current is zero,the virtual space vector pulse width modulation(VSVPWM)scheme does not cause the neutral-point voltage offset,but it lacks the ability to balance the deviation.For this reason,a neutral-point potential control strategy combining virtual space vector modulation and loop width control is proposed.The neutral-point potential is balanced by introducing the distribution factor for the regions with redundant vectors.For other regions,the potential is controlled by selecting a suitable switching sequence.Meanwhile,the effect on the virtual vector modulation is reduced within the loop width by setting an appropriate loop width,thereby improving the balance effect.The simulation results show that the proposed method has a strong ability to control the offset and has excellent potential balance performance under the conditions of balanced load,unbalanced load and asymmetric capacitance parameters.展开更多
The increasing overlap of core and colony populations during the anaphase of evolution may limit the performance of shifting balance genetic algorithms. To decrease such overlapping,so as to increase the local search ...The increasing overlap of core and colony populations during the anaphase of evolution may limit the performance of shifting balance genetic algorithms. To decrease such overlapping,so as to increase the local search capability of the core population,the sub-space method was used to generate uniformly distributed initial colony populations over the decision variable space. The core population was also dynamically divided,making simultaneous searching in several local spaces possible. The algorithm proposed in this paper was compared to the original one by searching for the optimum of a complicated multi-modal function. The results indicate that the solutions obtained by the modified algorithm are better than those of the original algorithm.展开更多
Numerous clothing enterprises in the market have a relatively low efficiency of assembly line planning due to insufficient optimization of bottleneck stations.As a result,the production efficiency of the enterprise is...Numerous clothing enterprises in the market have a relatively low efficiency of assembly line planning due to insufficient optimization of bottleneck stations.As a result,the production efficiency of the enterprise is not high,and the production organization is not up to expectations.Aiming at the problem of flexible process route planning in garment workshops,a multi-object genetic algorithm is proposed to solve the assembly line bal-ance optimization problem and minimize the machine adjustment path.The encoding method adopts the object-oriented path representation method,and the initial population is generated by random topology sorting based on an in-degree selection mechanism.The multi-object genetic algorithm improves the mutation and crossover operations according to the characteristics of the clothing process to avoid the generation of invalid offspring.In the iterative process,the bottleneck station is optimized by reasonable process splitting,and process allocation conforms to the strict limit of the station on the number of machines in order to improve the compilation efficiency.The effectiveness and feasibility of the multi-object genetic algorithm are proven by the analysis of clothing cases.Compared with the artificial allocation process,the compilation efficiency of MOGA is increased by more than 15%and completes the optimization of the minimum machine adjustment path.The results are in line with the expected optimization effect.展开更多
文摘The uncertain nature of mapping user tasks to Virtual Machines(VMs) causes system failure or execution delay in Cloud Computing.To maximize cloud resource throughput and decrease user response time,load balancing is needed.Possible load balancing is needed to overcome user task execution delay and system failure.Most swarm intelligent dynamic load balancing solutions that used hybrid metaheuristic algorithms failed to balance exploitation and exploration.Most load balancing methods were insufficient to handle the growing uncertainty in job distribution to VMs.Thus,the Hybrid Spotted Hyena and Whale Optimization Algorithm-based Dynamic Load Balancing Mechanism(HSHWOA) partitions traffic among numerous VMs or servers to guarantee user chores are completed quickly.This load balancing approach improved performance by considering average network latency,dependability,and throughput.This hybridization of SHOA and WOA aims to improve the trade-off between exploration and exploitation,assign jobs to VMs with more solution diversity,and prevent the solution from reaching a local optimality.Pysim-based experimental verification and testing for the proposed HSHWOA showed a 12.38% improvement in minimized makespan,16.21% increase in mean throughput,and 14.84% increase in network stability compared to baseline load balancing strategies like Fractional Improved Whale Social Optimization Based VM Migration Strategy FIWSOA,HDWOA,and Binary Bird Swap.
基金Supported by National Natural Science Foundation of China(Grant Nos.51275366,50875190,51305311)Specialized Research Fund for the Doctoral Program of Higher Education of China(Grant No.20134219110002)
文摘Due to the NP-hardness of the two-sided assembly line balancing （TALB） problem, multiple constraints existing in real applications are less studied, especially when one task is involved with several constraints. In this paper, an effective hybrid algorithm is proposed to address the TALB problem with multiple constraints （TALB-MC）. Considering the discrete attribute of TALB-MC and the continuous attribute of the standard teaching-learning-based optimization （TLBO） algorithm, the random-keys method is hired in task permutation representation, for the purpose of bridging the gap between them. Subsequently, a special mechanism for handling multiple constraints is developed. In the mechanism, the directions constraint of each task is ensured by the direction check and adjustment. The zoning constraints and the synchronism constraints are satisfied by teasing out the hidden correlations among constraints. The positional constraint is allowed to be violated to some extent in decoding and punished in cost fimction. Finally, with the TLBO seeking for the global optimum, the variable neighborhood search （VNS） is further hybridized to extend the local search space. The experimental results show that the proposed hybrid algorithm outperforms the late acceptance hill-climbing algorithm （LAHC） for TALB-MC in most cases, especially for large-size problems with multiple constraints, and demonstrates well balance between the exploration and the exploitation. This research proposes an effective and efficient algorithm for solving TALB-MC problem by hybridizing the TLBO and VNS.
基金supported by the National Natural Science Foundation of China(No.6217011238 and No.61931011).
文摘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.
基金supported by Scientific Research Foundation for the Returned Overseas Chinese ScholarsState Education Ministry under Grant No.2010-2011 and Chinese Post-doctoral Research Foundation
文摘One of the challenging scheduling problems in Cloud data centers is to take the allocation and migration of reconfigurable virtual machines as well as the integrated features of hosting physical machines into consideration. We introduce a Dynamic and Integrated Resource Scheduling algorithm (DAIRS) for Cloud data centers. Unlike traditional load-balance scheduling algorithms which often consider only one factor such as the CPU load in physical servers, DAIRS treats CPU, memory and network bandwidth integrated for both physical machines and virtual machines. We develop integrated measurement for the total imbalance level of a Cloud datacenter as well as the average imbalance level of each server. Simulation results show that DAIRS has good performance with regard to total imbalance level, average imbalance level of each server, as well as overall running time.
基金support and help of many individuals in the SASTRA University
文摘In a manufacturing industry, mixed model assembly line(MMAL) is preferred in order to meet the variety in product demand. MMAL balancing helps in assembling products with similar characteristics in a random fashion. The objective of this work aims in reducing the number of workstations, work load index between stations and within each station. As manual contribution of workers in final assembly line is more, ergonomics is taken as an additional objective function. Ergonomic risk level of a workstation is evaluated using a parameter called accumulated risk posture(ARP), which is calculated using rapid upper limb assessment(RULA) check sheet. This work is based on the case study of an MMAL problem in Rane(Madras) Ltd.(India), in which a problem based genetic algorithm(GA) has been proposed to minimize the mentioned objectives. The working of the genetic operators such as selection, crossover and mutation has been modified with respect to the addressed MMAL problem. The results show that there is a significant impact over productivity and the process time of the final assembled product, i.e., the rate of production is increased by 39.5% and the assembly time for one particular model is reduced to 13 min from existing 18 min. Also, the space required using the proposed assembly line is only 200 m2 against existing 350 m2. Further, the algorithm helps in reducing workers fatigue(i.e., ergonomic friendly).
文摘The growing global competition compels manufacturing organizations to engage themselves in all productivity improvement activities. In this direction, the consideration of mixed-model assembly line balancing problem and implementing in industries plays a major role in improving organizational productivity. In this paper, the mixed model assembly line balancing problem with deterministic task times is considered. The authors made an attempt to develop a genetic algorithm for realistic design of the mixed-model assembly line balancing problem. The design is made using the originnal task times of the models, which is a realistic approach. Then, it is compared with the generally perceived design of the mixed-model assembly line balancing problem.
文摘Task scheduling in highly elastic and dynamic processing environments such as cloud computing have become the most discussed problem among researchers.Task scheduling algorithms are responsible for the allocation of the tasks among the computing resources for their execution,and an inefficient task scheduling algorithm results in under-or over-utilization of the resources,which in turn leads to degradation of the services.Therefore,in the proposed work,load balancing is considered as an important criterion for task scheduling in a cloud computing environment as it can help in reducing the overhead in the critical decision-oriented process.In this paper,we propose an adaptive genetic algorithm-based load balancing(GALB)-aware task scheduling technique that not only results in better utilization of resources but also helps in optimizing the values of key performance indicators such as makespan,performance improvement ratio,and degree of imbalance.The concept of adaptive crossover and mutation is used in this work which results in better adaptation for the fittest individual of the current generation and prevents them from the elimination.CloudSim simulator has been used to carry out the simulations and obtained results establish that the proposed GALB algorithm performs better for all the key indicators and outperforms its peers which are taken into the consideration.
基金Supported by the National Natural Science Foundation of China (No. 60672059, 60496315 )the National High Technology Research and Development Programme of China (No.2006AA01Z233)
文摘Because of different system capacities of base station （BS） or access point （AP） and ununiformity of traffic distribution in different cells, quantities of new call users may be blocked in overloaded cell in communication hot spots. Whereas in some neighboring under-loaded cells, bandwidth may be superfluous because there are only few users to request services. In order to raise resource utilization of the whole heterogeneous networks, several novel load balancing strategies are proposed, which combine the call ad- mission control policy and multi-hop routing protocol of ad-hoc network for load balancing. These loadbalancing strategies firstly make a decision whether to admit a new call or not by considering some parameters like load index and route cost, etc., and then transfer the denied users into neighboring under-loaded cell with surplus channel according to optimum multi-hop routing algorithm. Simulation results show that the proposed load balancing strategies can distribute traffics to the whole heterogeneous wireless netorks, improve the load balance index efficiently, and avoid the call block phenomenon almost absolutely.
基金Supported by the 12th Five-Year Plan National Pre-research Program of Chinathe Aerospace Science Foundation of China(20111652016)+1 种基金the China Postdoctoral Science Foundation(2012M511748)the Jiangsu Planned Projects for Postdoctoral Research Funds(1102053C)
文摘Aiming at assembly line balancing problem,a double chromosome genetic algorithm(DCGA)is proposed to avoid trapping in local optimum,which is a disadvantage of standard genetic algorithm(SGA).In this algorithm,there are two chromosomes of each individual,and the better one,regarded as dominant chromosome,determines the fitness.Dominant chromosome keeps excellent gene segments to speed up the convergence,and recessive chromosome maintains population diversity to get better global search ability to avoid local optimal solution.When the amounts of chromosomes are equal,the population size of DCGA is half that of SGA,which significantly reduces evolutionary time.Finally,the effectiveness is verified by experiments.
文摘Recently,the fundamental problem with Hybrid Mobile Ad-hoc Net-works(H-MANETs)is tofind a suitable and secure way of balancing the load through Internet gateways.Moreover,the selection of the gateway and overload of the network results in packet loss and Delay(DL).For optimal performance,it is important to load balance between different gateways.As a result,a stable load balancing procedure is implemented,which selects gateways based on Fuzzy Logic(FL)and increases the efficiency of the network.In this case,since gate-ways are selected based on the number of nodes,the Energy Consumption(EC)was high.This paper presents a novel Node Quality-based Clustering Algo-rithm(NQCA)based on Fuzzy-Genetic for Cluster Head and Gateway Selection(FGCHGS).This algorithm combines NQCA with the Improved Weighted Clus-tering Algorithm(IWCA).The NQCA algorithm divides the network into clusters based upon node priority,transmission range,and neighbourfidelity.In addition,the simulation results tend to evaluate the performance effectiveness of the FFFCHGS algorithm in terms of EC,packet loss rate(PLR),etc.
文摘This work investigates a multi-product parallel disassembly line balancing problem considering multi-skilled workers.A mathematical model for the parallel disassembly line is established to achieve maximized disassembly profit and minimized workstation cycle time.Based on a product’s AND/OR graph,matrices for task-skill,worker-skill,precedence relationships,and disassembly correlations are developed.A multi-objective discrete chemical reaction optimization algorithm is designed.To enhance solution diversity,improvements are made to four reactions:decomposition,synthesis,intermolecular ineffective collision,and wall invalid collision reaction,completing the evolution of molecular individuals.The established model and improved algorithm are applied to ball pen,flashlight,washing machine,and radio combinations,respectively.Introducing a Collaborative Resource Allocation(CRA)strategy based on a Decomposition-Based Multi-Objective Evolutionary Algorithm,the experimental results are compared with four classical algorithms:MOEA/D,MOEAD-CRA,Non-dominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ),and Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ).This validates the feasibility and superiority of the proposed algorithm in parallel disassembly production lines.
基金Supported by the National High Technology Research and Development Programme of China （No. 2006AA04Z160） and the National Natural Science Foundation of China （ No. 60874066）.
文摘A balancing problem for a mixed model assembly line with uncertain task processmg Ume anO daily model mixed changes is considered, and the objective is to minimize the work variances between stations in the line. For the balancing problem for the scenario-based robust assembly line with a finitely large number of potential scenarios, the direct solution methodology considering all potential scenarios is quite time-consuming. A scenario relaxation algorithm that embeds genetic al- gorithm is developed. This new algorithm guarantees termination at an optimal robust solution with relatively short running time, and makes it possible to solve robust problems with large quantities of potential scenarios. Extensive computational results are reported to show the efficiency and effectiveness of the proposed algorithm.
基金Project(61074018)supported by the National Natural Science Foundation of ChinaProject(2012kfjj06)supported by Hunan Province Key Laboratory of Smart Grids Operation and Control(Changsha University of Science and Technology),China
文摘A comprehensive predictive strategy was proposed for the neutral-point balancing control of back-to-back three-level converters. The phase currents at both sides and the DC-link capacitor voltages were measured for the prediction of the neutral-point current. A quality function was found to balance the neutral-point, and a metabolic on-times distribution factor was used as a predicator to minimize the quality function at each switching state. Simulation results show that the proposed method produces smaller ripples in tested signals compared with the established one, namely, 9.15% less in a total harmonic distortion(THD) of line-to-line voltage, 1.08% less in the THD of phase current, and 0.9 V less in the ripple of the neutral-point voltage. The obtained experimental results show that the main harmonics of the line-to-line voltage and the phase current in the proposed method are improved by 10 d B and 6 d B, respectively, and the ripple of neutral-point voltage is halved compared to the established one.
文摘Cloud Computing has the ability to provide on-demand access to a shared resource pool.It has completely changed the way businesses are managed,implement applications,and provide services.The rise in popularity has led to a significant increase in the user demand for services.However,in cloud environments efficient load balancing is essential to ensure optimal performance and resource utilization.This systematic review targets a detailed description of load balancing techniques including static and dynamic load balancing algorithms.Specifically,metaheuristic-based dynamic load balancing algorithms are identified as the optimal solution in case of increased traffic.In a cloud-based context,this paper describes load balancing measurements,including the benefits and drawbacks associated with the selected load balancing techniques.It also summarizes the algorithms based on implementation,time complexity,adaptability,associated issue(s),and targeted QoS parameters.Additionally,the analysis evaluates the tools and instruments utilized in each investigated study.Moreover,comparative analysis among static,traditional dynamic and metaheuristic algorithms based on response time by using the CloudSim simulation tool is also performed.Finally,the key open problems and potential directions for the state-of-the-art metaheuristic-based approaches are also addressed.
文摘Influence coefficient method and the modal balancing method are often used in the dynamic balancing in the past days. These methods sometimes exist a lot of big measurement errors. So, in order to make these errors much smaller, and to use the vibration information of the rotor more sufficiently, at last, we put forward the full vector dynamic balancing algorithm. Though the theoretical analysis, and the experiment tests, we can compare with the new method and the old method , study the relationship between the dynamic balancing and the rotation equipment, and the direction of the development. The full vector dynamic balancing algorithm theory can be inferred from the Jeffcott rotor. To compare with the methods which are mentioned before, we can find that the full vector dynamic balancing algorithm is much better than the influence coefficient method and the modal balancing method. We can use the MATLAB program to prove that the full vector dynamic balancing algorithm is much better. So the conclusion is completely right.
基金Supported by the National Natural Science Foundation of China(61073063,61173029,61272182 and 61173030)the Ocean Public Welfare Scientific Research Project of State Oceanic Administration of China(201105033)National Digital Ocean Key Laboratory Open Fund Projects(KLDO201306)
文摘Aiming at the load imbalance and poor scalability in single-tier Web server clusters, an efficient load balancing ap- proach is proposed for constructing an N-hierarchical （multi-tier） Web server cluster. In each layer, multiple load balancers are set to receive the user requests simultaneously, and different load bal- ancing algorithms are used to construct the high-scalable Web cluster system. At the same time, an improved load balancing al- gorithm is proposed, which can dynamically calculate weights according to the utilization of the server resources, and reasonably distribute the loads for each server according to the load status of the servers. The experimental results show that the proposed ap- proach can greatly decrease the load imbalance among the Web servers and reduce the response time of the entire Web cluster system.
基金Under the auspices of National Basic Research Program of China (No. 2010CB951304-5)National Natural Science Foundation of China (No. 41101545,41030743)
文摘In this study,the Surface Energy Balance Algorithms for Land(SEBAL) model and Moderate Resolution Imaging Spectroradiometer(MODIS) products from Terra satellite were combined with meteorological data to estimate evapotranspiration(ET) over the Sanjiang Plain,Northeast China.Land cover/land use was classified by using a recursive partitioning and regression tree with MODIS Normalized Difference Vegetation Index(NDVI) time series data,which were reconstructed based on the Savitzky-Golay filtering approach.The MODIS product Quality Assessment Science Data Sets(QA-SDS) was analyzed and all scenes with valid data covering more than 75% of the Sanjiang Plain were selected for the SEBAL modeling.This provided 12 overpasses during 184-day growing season from May 1st to October 31st,2006.Daily ET estimated by the SEBAL model was misestimaed at the range of-11.29% to 27.57% compared with that measured by Eddy Covariance system(10.52% on average).The validation results show that seasonal ET from the SEBAL model is comparable to that from ground observation within 8.86% of deviation.Our results reveal that the time series daily ET of different land cover/use increases from vegetation on-going until June or July and then decreases as vegetation senesced.Seasonal ET is lower in dry farmland(average(Ave):491 mm) and paddy field(Ave:522 mm) and increases in wetlands to more than 586 mm.As expected,higher seasonal ET values are observed for the Xingkai Lake in the southeastern part of the Sanjiang Plain(Ave:823 mm),broadleaf forest(Ave:666 mm) and mixed wood(Ave:622 mm) in the southern/western Sanjiang Plain.The ET estimation with SEBAL using MODIS products can provide decision support for operational water management issues.
基金National Natural Science Foundation of China(No.61761027)Postgraduate Education Reform Project of Lanzhou Jiaotong University(No.1600120101)
文摘The topology of diode neutral-point-clamped(NPC)three-level inverter is prone to neutral-point potential offset.When the sum of three-phase current is zero,the virtual space vector pulse width modulation(VSVPWM)scheme does not cause the neutral-point voltage offset,but it lacks the ability to balance the deviation.For this reason,a neutral-point potential control strategy combining virtual space vector modulation and loop width control is proposed.The neutral-point potential is balanced by introducing the distribution factor for the regions with redundant vectors.For other regions,the potential is controlled by selecting a suitable switching sequence.Meanwhile,the effect on the virtual vector modulation is reduced within the loop width by setting an appropriate loop width,thereby improving the balance effect.The simulation results show that the proposed method has a strong ability to control the offset and has excellent potential balance performance under the conditions of balanced load,unbalanced load and asymmetric capacitance parameters.
基金Project 60575046 supported by the National Natural Science Foundation of China
文摘The increasing overlap of core and colony populations during the anaphase of evolution may limit the performance of shifting balance genetic algorithms. To decrease such overlapping,so as to increase the local search capability of the core population,the sub-space method was used to generate uniformly distributed initial colony populations over the decision variable space. The core population was also dynamically divided,making simultaneous searching in several local spaces possible. The algorithm proposed in this paper was compared to the original one by searching for the optimum of a complicated multi-modal function. The results indicate that the solutions obtained by the modified algorithm are better than those of the original algorithm.
基金supported by Key R&D project of Zhejiang Province (2018C01005),http://kjt.zj.gov.cn/.
文摘Numerous clothing enterprises in the market have a relatively low efficiency of assembly line planning due to insufficient optimization of bottleneck stations.As a result,the production efficiency of the enterprise is not high,and the production organization is not up to expectations.Aiming at the problem of flexible process route planning in garment workshops,a multi-object genetic algorithm is proposed to solve the assembly line bal-ance optimization problem and minimize the machine adjustment path.The encoding method adopts the object-oriented path representation method,and the initial population is generated by random topology sorting based on an in-degree selection mechanism.The multi-object genetic algorithm improves the mutation and crossover operations according to the characteristics of the clothing process to avoid the generation of invalid offspring.In the iterative process,the bottleneck station is optimized by reasonable process splitting,and process allocation conforms to the strict limit of the station on the number of machines in order to improve the compilation efficiency.The effectiveness and feasibility of the multi-object genetic algorithm are proven by the analysis of clothing cases.Compared with the artificial allocation process,the compilation efficiency of MOGA is increased by more than 15%and completes the optimization of the minimum machine adjustment path.The results are in line with the expected optimization effect.
