Purpose-Current industrial scenario is largely dependent on cloud computing paradigms.On-demand services provided by cloud data centre are paid as per use.Hence,it is very important to make use of the allocated resour...Purpose-Current industrial scenario is largely dependent on cloud computing paradigms.On-demand services provided by cloud data centre are paid as per use.Hence,it is very important to make use of the allocated resources to the maximum.The resource utilization is highly dependent on the allocation of resources to the incoming request.The allocation of requests is done with respect to the physical machines present in the datacenter.While allocating the tasks to these physical machines,it needs to be allocated in such a way that no physical machine is underutilized or over loaded.To make sure of this,optimal load balancing is very important.Design/methodology/approach-The paper proposes an algorithm which makes use of the fitness functions and duopoly game theory to allocate the tasks to the physical machines which can handle the resource requirement of the incoming tasks.The major focus of the proposed work is to optimize the load balancing in a datacenter.When optimization happens,none of the physical machine is neither overloaded nor under-utilized,hence resulting in efficient utilization of the resources.Findings-The performance of the proposed algorithm is compared with different existing load balancing algorithms such as round-robin load(RR)ant colony optimization(ACO),artificial bee colony(ABC)with respect to the selected parameters response time,virtual machine migrations,host shut down and energy consumption.All the four parameters gave a positive result when the algorithm is simulated.Originality/value-The contribution of this paper is towards the domain of cloud load balancing.The paper is proposing a novel approach to optimize the cloud load balancing process.The results obtained show that response time,virtual machine migrations,host shut down and energy consumption are reduced in comparison to few of the existing algorithms selected for the study.The proposed algorithm based on the duopoly function and fitness function brings in an optimized performance compared to the four algorithms analysed.展开更多
To enable optimal navigation for unmanned surface vehicle(USV),we proposed an adaptive hybrid strategy-based sparrow search algorithm(SSA)for efficient and reliable path planning.The proposed method began by enhancing...To enable optimal navigation for unmanned surface vehicle(USV),we proposed an adaptive hybrid strategy-based sparrow search algorithm(SSA)for efficient and reliable path planning.The proposed method began by enhancing the fitness function to comprehensively account for critical path planning metrics,including path length,turning angle,and navigation safety.To improve search diversity and effectively avoid premature convergence to local optima,chaotic mapping was employed during the population initialization stage,allowing the algorithm to explore a wider solution space from the outset.A reverse inertia weight mechanism was introduced to dynamically balance exploration and exploitation across different iterations.The adaptive adjustment of the inertia weight further improved convergence efficiency and enhanced global optimization performance.In addition,a Cauchy-Gaussian hybrid update strategy was incorporated to inject randomness and variation into the search process,which helped the algorithm escape local minima and maintain a high level of solution diversity.This approach significantly enhanced the robustness and adaptability of the optimization process.Simulation experiments confirmed that the improved SSA consistently outperformed benchmark algorithms such as the original SSA,PSO,and WMR-SSA.Compared with the three algorithms,in the simulated sea area,the path lengths of the proposed algorithm are reduced by 21%,21%,and 16%,respectively,and under the actual sea simulation conditions,the path lengths are reduced by 13%,15%,and 11%,respectively.The results highlighted the effectiveness and practicality of the proposed method,providing an effective solution for intelligent and autonomous USV navigation in complex ocean environments.展开更多
The learning algorithms of causal discovery mainly include score-based methods and genetic algorithms(GA).The score-based algorithms are prone to searching space explosion.Classical GA is slow to converge,and prone to...The learning algorithms of causal discovery mainly include score-based methods and genetic algorithms(GA).The score-based algorithms are prone to searching space explosion.Classical GA is slow to converge,and prone to falling into local optima.To address these issues,an improved GA with domain knowledge(IGADK)is proposed.Firstly,domain knowledge is incorporated into the learning process of causality to construct a new fitness function.Secondly,a dynamical mutation operator is introduced in the algorithm to accelerate the convergence rate.Finally,an experiment is conducted on simulation data,which compares the classical GA with IGADK with domain knowledge of varying accuracy.The IGADK can greatly reduce the number of iterations,populations,and samples required for learning,which illustrates the efficiency and effectiveness of the proposed algorithm.展开更多
Background:The Functional Movement Screen(FMS^(TM)) has become increasingly popular for identifying functional limitations in basic functional movements.This exploratory and descriptive study was undertaken to confirm...Background:The Functional Movement Screen(FMS^(TM)) has become increasingly popular for identifying functional limitations in basic functional movements.This exploratory and descriptive study was undertaken to confirm feasibility of performing the FMS^(TM) in older active adults,assess prevalence of asymmetries and to evaluate the relationship between functional movement ability,age,physical activity levels and body mass index(BMI).Methods:This is an observational study;97 men(n = 53) and women(n = 44) between the ages of 52 and 83 participated.BMI was computed and self-reported physical activity levels were obtained.Subjects were grouped by age(5-year intervals),BMI(normal,over-weight,and obese)and sex.Each participant's performance on the FMS^(TM) was digitally recorded for later analysis.Results:The youngest age group(50–54 years) scored highest in all seven tests and the oldest age group(75+) scored lowest in most of the tests compared to all other age groups.The subjects in the 'normal weight' group performed no different than those who were in the 'overweight' group;both groups performed better than the 'obese' group.Of the 97 participants 54 had at least one asymmetry.The pairwise correlations between the total FMS^(TM) score and age(r =-0.531),BMI(r =-0.270),and the measure of activity level(r = 0.287) were significant(p < 0.01 for all).Conclusion:FMS^(TM) scores decline with increased BMI,increased age,and decreased activity level.The screen identifies range of motion-and strength-related asymmetries.The FMS^(TM) can be used to assess functional limitations and asymmetries.Future research should evaluate if a higher total FMS^(TM) score is related to fewer falls or injuries in the older population.展开更多
Forecasting crop yields based on remote sensing data is one of the most important tasks in agriculture.Soybean is the main crop in the Russian Far East.It is desirable to forecast soybean yield as early as possible wh...Forecasting crop yields based on remote sensing data is one of the most important tasks in agriculture.Soybean is the main crop in the Russian Far East.It is desirable to forecast soybean yield as early as possible while maintaining high accuracy.This study aimed to investigate seasonal time series of the normalized difference vegetation index(NDVI) to achieve early forecasting of soybean yield.This research used data from the Moderate Resolution Image Spectroradiometer(MODIS),an arable-land mask obtained from the VEGA-Science web service,and soybean yield data for 2008-2017 for the Jewish Autonomous Region(JAR) districts.Four approximating functions were fitted to model the NDVI time series:Gaussian,double logistic(DL),and quadratic and cubic polynomials.In the period from calendar weeks 22-42(end of May to mid-October),averaged over two districts,the model using the DL function showed the highest accuracy(mean absolute percentage error-4.0%,root mean square error(RMSE)-0.029,P <0.01).The yield forecast accuracy of prediction in the period of weeks 25-30 in JAR municipalities using the parameters of the Gaussian function was higher(P <0.05) than that using the other functions.The mean forecast error for the Gaussian function was 14.9% in week 25(RMSE was0.21 t ha) and 5.1%-12.9% in weeks 26-30(RMSE varied from 0.06 to 0.15 t ha) according to the2013-2017 data.In weeks 31-32,the error was 5.0%-5.4%(RMSE was 0.07 t ha) using the Gaussian parameters and 7.4%-7.7%(RMSE was 0.09-0.11 t ha) for the DL function.When the method was applied to municipal districts of other soy-producing regions of the Russian Far East.RMSE was0.14-0.32 t hain weeks 25-26 and did not exceed 0.20 t hain subsequent weeks.展开更多
Based on the existing continuous borehole strain observation,the multiquadric function fitting method was used to deal with time series data. The impact of difference kernel function parameters was discussed to obtain...Based on the existing continuous borehole strain observation,the multiquadric function fitting method was used to deal with time series data. The impact of difference kernel function parameters was discussed to obtain a valuable fitting result,from which the physical connotation of the original data and its possible applications were analyzed.Meanwhile,a brief comparison was made between the results of multiquadric function fitting and polynomial fitting.展开更多
The aim of this study was to determine differences in body composition and physical fitness in elderly men and women. Five hundred twenty six subjects were included in this study, 272 were men （52%） and 254 women （...The aim of this study was to determine differences in body composition and physical fitness in elderly men and women. Five hundred twenty six subjects were included in this study, 272 were men （52%） and 254 women （48%）. To determine the trend of changes of anthropometric parameters and physical fitness in people aged over 60, the authors were divided subjects in 5 age groups： 60-64, 65-69, 70-74, 75-79 and over 80 years of age. Decrease in strength is observed with the aging process so that the respondents aged 60-64 years significantly differ in the strength of the lower extremities of elderly subjects 70-74 and 75-79 years of age. Also this difference is noticeable if they compare with men and women. In this study, they found that there was an increase in the amount of adipose tissue, reducing the level of muscle activity and decreased muscle strength and endurance of the aging process.展开更多
Particle swarm optimization （PSO） is a new heuristic algorithm which has been applied to many optimization problems successfully. Attribute reduction is a key studying point of the rough set theory, and it has been ...Particle swarm optimization （PSO） is a new heuristic algorithm which has been applied to many optimization problems successfully. Attribute reduction is a key studying point of the rough set theory, and it has been proven that computing minimal reduc- tion of decision tables is a non-derterministic polynomial （NP）-hard problem. A new cooperative extended attribute reduction algorithm named Co-PSAR based on improved PSO is proposed, in which the cooperative evolutionary strategy with suitable fitness func- tions is involved to learn a good hypothesis for accelerating the optimization of searching minimal attribute reduction. Experiments on Benchmark functions and University of California, Irvine （UCI） data sets, compared with other algorithms, verify the superiority of the Co-PSAR algorithm in terms of the convergence speed, efficiency and accuracy for the attribute reduction.展开更多
Based on the bat algorithm(BA), this paper proposes a discrete BA(DBA) approach to optimize the disassembly sequence planning(DSP) problem, for the purpose of obtaining an optimum disassembly sequence(ODS) of a produc...Based on the bat algorithm(BA), this paper proposes a discrete BA(DBA) approach to optimize the disassembly sequence planning(DSP) problem, for the purpose of obtaining an optimum disassembly sequence(ODS) of a product with a high degree of automation and guiding maintenance operation. The BA for solving continuous problems is introduced, and combining with mathematical formulations, the BA is reformed to be the DBA for DSP problems. The fitness function model(FFM) is built to evaluate the quality of disassembly sequences. The optimization performance of the DBA is tested and verified by an application case, and the DBA is compared with the genetic algorithm(GA), particle swarm optimization(PSO) algorithm and differential mutation BA(DMBA). Numerical experiments show that the proposed DBA has a better optimization capability and provides more accurate solutions than the other three algorithms.展开更多
Particle filter(PF) can solve the problem of state estimation under strong non-linear non-Gaussian noise condition with respect to traditional Kalman filter(KF) and those improved KFs such as extended KF(EKF) and unsc...Particle filter(PF) can solve the problem of state estimation under strong non-linear non-Gaussian noise condition with respect to traditional Kalman filter(KF) and those improved KFs such as extended KF(EKF) and unscented KF(UKF). However, problems such as particle depletion and particle degradation affect the performance of PF. Optimizing the particle set to high likelihood region with intelligent optimization algorithm results in a more reasonable distribution of the sampling particles and more accurate state estimation. In this paper, a novel bird swarm algorithm based PF(BSAPF) is presented. Firstly, different behavior models are established by emulating the predation, flight, vigilance and follower behavior of the birds. Then, the observation information is introduced into the optimization process of the proposal distribution with the design of fitness function. In order to prevent particles from getting premature(being stuck into local optimum) and increase the diversity of particles, Lévy flight is designed to increase the randomness of particle's movement. Finally,the proposed algorithm is applied to estimate the speed of the train under the condition that the measurement noise of the wheel sensor is non-Gaussian distribution. Simulation study and experimental results both show that BSAPF is more accurate and has more effective particle number as compared with PF and UKF, demonstrating the promising performance of the method.展开更多
Mobile robot global path planning in a static environment is an important problem. The paper proposes a method of global path planning based on neural network and genetic algorithm. We constructed the neural network m...Mobile robot global path planning in a static environment is an important problem. The paper proposes a method of global path planning based on neural network and genetic algorithm. We constructed the neural network model of environmental information in the workspace for a robot and used this model to establish the relationship between a collision avoidance path and the output of the model. Then the two-dimensional coding for the path via-points was converted to one-dimensional one and the fitness of both the collision avoidance path and the shortest distance are integrated into a fitness function. The simulation results showed that the proposed method is correct and effective.展开更多
Recently,vehicular ad hoc networks(VANETs)finds applicability in different domains such as security,rescue operations,intelligent transportation systems(ITS),etc.VANET has unique features like high mobility,limited mo...Recently,vehicular ad hoc networks(VANETs)finds applicability in different domains such as security,rescue operations,intelligent transportation systems(ITS),etc.VANET has unique features like high mobility,limited mobility patterns,adequate topologymodifications,and wireless communication.Despite the benefits of VANET,scalability is a challenging issue which could be addressed by the use of cluster-based routing techniques.It enables the vehicles to perform intercluster communication via chosen CHs and optimal routes.The main drawback of VANET network is the network unsteadiness that results in minimum lifetime.In order to avoid reduced network lifetime in VANET,this paper presents an enhanced metaheuristics based clustering with multihop routing technique for lifetime maximization(EMCMHR-LM)in VANET.The presented EMCMHR-LM model involves the procedure of arranging clusters,cluster head(CH)selection,and route selection appropriate for VANETs.The presentedEMCMHR-LMmodel uses slime mold optimization based clustering(SMO-C)technique to group the vehicles into clusters.Besides,an enhanced wild horse optimization based multihop routing(EWHO-MHR)protocol by the optimization of network parameters.The presented EMCMHR-LMmodel is simulated usingNetwork Simulator(NS3)tool and the simulation outcomes reported the enhanced performance of the proposed EMCMHR-LM technique over the other models.展开更多
In order to reduce the computation of complex problems, a new surrogate-assisted estimation of distribution algorithm with Gaussian process was proposed. Coevolution was used in dual populations which evolved in paral...In order to reduce the computation of complex problems, a new surrogate-assisted estimation of distribution algorithm with Gaussian process was proposed. Coevolution was used in dual populations which evolved in parallel. The search space was projected into multiple subspaces and searched by sub-populations. Also, the whole space was exploited by the other population which exchanges information with the sub-populations. In order to make the evolutionary course efficient, multivariate Gaussian model and Gaussian mixture model were used in both populations separately to estimate the distribution of individuals and reproduce new generations. For the surrogate model, Gaussian process was combined with the algorithm which predicted variance of the predictions. The results on six benchmark functions show that the new algorithm performs better than other surrogate-model based algorithms and the computation complexity is only 10% of the original estimation of distribution algorithm.展开更多
In many real-world applications of evolutionary algorithms,the fitness of an individual requires a quantitative measure.This paper proposes a self-adaptive linear evolutionary algorithm （ALEA） in which we introduce ...In many real-world applications of evolutionary algorithms,the fitness of an individual requires a quantitative measure.This paper proposes a self-adaptive linear evolutionary algorithm （ALEA） in which we introduce a novel strategy for evaluating individual＇s relative strengths and weaknesses.Based on this strategy,searching space of constrained optimization problems with high dimensions for design variables is compressed into two-dimensional performance space in which it is possible to quickly identify ＇good＇ individuals of the performance for a multiobjective optimization application,regardless of original space complexity.This is considered as our main contribution.In addition,the proposed new evolutionary algorithm combines two basic operators with modification in reproduction phase,namely,crossover and mutation.Simulation results over a comprehensive set of benchmark functions show that the proposed strategy is feasible and effective,and provides good performance in terms of uniformity and diversity of solutions.展开更多
The three-layer forward neural networks are used to establish the inverse kinematics models of robot manipulators. The fuzzy genetic algorithm based on the linear scaling of the fitness value is presented to update th...The three-layer forward neural networks are used to establish the inverse kinematics models of robot manipulators. The fuzzy genetic algorithm based on the linear scaling of the fitness value is presented to update the weights of neural networks. To increase the search speed of the algorithm, the crossover probability and the mutation probability are adjusted through fuzzy control and the fitness is modified by the linear scaling method in FGA. Simulations show that the proposed method improves considerably the precision of the inverse kinematics solutions for robot manipulators and guarantees a rapid global convergence and overcomes the drawbacks of SGA and the BP algorithm.展开更多
All the parameters of beamforming are usually optimized simultaneously in implementing the optimization of antenna array pattern with multiple objectives and parameters by genetic algorithms （GAs）. Firstly, this pap...All the parameters of beamforming are usually optimized simultaneously in implementing the optimization of antenna array pattern with multiple objectives and parameters by genetic algorithms （GAs）. Firstly, this paper analyzes the performance of fitness functions of previous algorithms. It shows that original algorithms make the fitness functions too complex leading to large amount of calculation, and also the selection of the weight of parameters very sensitive due to many parameters optimized simultaneously. This paper proposes a kind of algorithm of composite beamforming, which detaches the antenna array into two parts corresponding to optimization of different objective parameters respectively. New algorithm substitutes the previous complex fitness function with two simpler functions. Both theoretical analysis and simulation results show that this method simplifies the selection of weighting parameters and reduces the complexity of calculation. Furthermore, the algorithm has better performance in lowering side lobe and interferences in comparison with conventional algorithms of beamforming in the case of slightly widening the main lobe.展开更多
As one of the typical method for side channel attack,DPA has become a serious trouble for the security of encryption algorithm implementation.The potential capability of DPA attack induces researchers making a lot of ...As one of the typical method for side channel attack,DPA has become a serious trouble for the security of encryption algorithm implementation.The potential capability of DPA attack induces researchers making a lot of efforts in this area,which significantly improved the attack efficiency of DPA.However,most of these efforts were made based on the hypothesis that the gathered power consumption data from the target device were stable and low noise.If large deviation happens in part of the power consumption data sample,the efficiency of DPA attack will be reduced rapidly.In this work,a highly efficient method for DPA attack is proposed with the inspiration of genetic algorithm.Based on the designed fitness function,power consumption data that is stable and less noisy will be selected and the noisy ones will be eliminated.In this way,not only improves the robustness and efficiency of DPA attack,but also reduces the number of samples needed.With experiments on block cipher algorithms of DES and SM4,10%and 12.5%of the number of power consumption curves have been reduced in average with the proposed DPAG algorithm compared to original DPA attack respectively.The high efficiency and correctness of the proposed algorithm and novel model are proved by experiments.展开更多
In order to solve the problem between searching performance and convergence of genetic algorithms, a fast genetic algorithm generalized self-adaptive genetic algorithm (GSAGA) is presented. (1) Evenly distributed init...In order to solve the problem between searching performance and convergence of genetic algorithms, a fast genetic algorithm generalized self-adaptive genetic algorithm (GSAGA) is presented. (1) Evenly distributed initial population is generated. (2) Superior individuals are not broken because of crossover and mutation operation for they are sent to subgeneration directly. (3) High quality im- migrants are introduced according to the condition of the population schema. (4) Crossover and mutation are operated on self-adaptation. Therefore, GSAGA solves the coordination problem between convergence and searching performance. In GSAGA, the searching per- formance and global convergence are greatly improved compared with many existing genetic algorithms. Through simulation, the val- idity of this modified genetic algorithm is proved.展开更多
In recent times,internet of things(IoT)applications on the cloud might not be the effective solution for every IoT scenario,particularly for time sensitive applications.A significant alternative to use is edge computi...In recent times,internet of things(IoT)applications on the cloud might not be the effective solution for every IoT scenario,particularly for time sensitive applications.A significant alternative to use is edge computing that resolves the problem of requiring high bandwidth by end devices.Edge computing is considered a method of forwarding the processing and communication resources in the cloud towards the edge.One of the considerations of the edge computing environment is resource management that involves resource scheduling,load balancing,task scheduling,and quality of service(QoS)to accomplish improved performance.With this motivation,this paper presents new soft computing based metaheuristic algorithms for resource scheduling(RS)in the edge computing environment.The SCBMARS model involves the hybridization of the Group Teaching Optimization Algorithm(GTOA)with rat swarm optimizer(RSO)algorithm for optimal resource allocation.The goal of the SCBMA-RS model is to identify and allocate resources to every incoming user request in such a way,that the client’s necessities are satisfied with the minimum number of possible resources and optimal energy consumption.The problem is formulated based on the availability of VMs,task characteristics,and queue dynamics.The integration of GTOA and RSO algorithms assist to improve the allocation of resources among VMs in the data center.For experimental validation,a comprehensive set of simulations were performed using the CloudSim tool.The experimental results showcased the superior performance of the SCBMA-RS model interms of different measures.展开更多
The nodes in the sensor network have a wide range of uses,particularly on under-sea links that are skilled for detecting,handling as well as management.The underwater wireless sensor networks support collecting pollut...The nodes in the sensor network have a wide range of uses,particularly on under-sea links that are skilled for detecting,handling as well as management.The underwater wireless sensor networks support collecting pollution data,mine survey,oceanographic information collection,aided navigation,strategic surveillance,and collection of ocean samples using detectors that are submerged inwater.Localization,congestion routing,and prioritizing the traffic is the major issue in an underwater sensor network.Our scheme differentiates the different types of traffic and gives every type of traffic its requirements which is considered regarding network resource.Minimization of localization error using the proposed angle-based forwarding scheme is explained in this paper.We choose the shortest path to the destination using the fitness function which is calculated based on fault ratio,dispatching of packets,power,and distance among the nodes.This work contemplates congestion conscious forwarding using hard stage and soft stage schemes which reduce the congestion by monitoring the status of the energy and buffer of the nodes and controlling the traffic.The study with the use of the ns3 simulator demonstrated that a given algorithm accomplishes superior performance for loss of packet,delay of latency,and power utilization than the existing algorithms.展开更多
文摘Purpose-Current industrial scenario is largely dependent on cloud computing paradigms.On-demand services provided by cloud data centre are paid as per use.Hence,it is very important to make use of the allocated resources to the maximum.The resource utilization is highly dependent on the allocation of resources to the incoming request.The allocation of requests is done with respect to the physical machines present in the datacenter.While allocating the tasks to these physical machines,it needs to be allocated in such a way that no physical machine is underutilized or over loaded.To make sure of this,optimal load balancing is very important.Design/methodology/approach-The paper proposes an algorithm which makes use of the fitness functions and duopoly game theory to allocate the tasks to the physical machines which can handle the resource requirement of the incoming tasks.The major focus of the proposed work is to optimize the load balancing in a datacenter.When optimization happens,none of the physical machine is neither overloaded nor under-utilized,hence resulting in efficient utilization of the resources.Findings-The performance of the proposed algorithm is compared with different existing load balancing algorithms such as round-robin load(RR)ant colony optimization(ACO),artificial bee colony(ABC)with respect to the selected parameters response time,virtual machine migrations,host shut down and energy consumption.All the four parameters gave a positive result when the algorithm is simulated.Originality/value-The contribution of this paper is towards the domain of cloud load balancing.The paper is proposing a novel approach to optimize the cloud load balancing process.The results obtained show that response time,virtual machine migrations,host shut down and energy consumption are reduced in comparison to few of the existing algorithms selected for the study.The proposed algorithm based on the duopoly function and fitness function brings in an optimized performance compared to the four algorithms analysed.
基金supported by Shandong Provincial Department of Science and Technology Project(No.2022C01246)National Undergraduate Innovation Training Project(Nos.202410390028,202310390026)+1 种基金Fujian Provincial Undergraduate Innovation Training Project(No.202410390093)Jimei University Innovation Training Project(Nos.2024xj224,2023xj179).
文摘To enable optimal navigation for unmanned surface vehicle(USV),we proposed an adaptive hybrid strategy-based sparrow search algorithm(SSA)for efficient and reliable path planning.The proposed method began by enhancing the fitness function to comprehensively account for critical path planning metrics,including path length,turning angle,and navigation safety.To improve search diversity and effectively avoid premature convergence to local optima,chaotic mapping was employed during the population initialization stage,allowing the algorithm to explore a wider solution space from the outset.A reverse inertia weight mechanism was introduced to dynamically balance exploration and exploitation across different iterations.The adaptive adjustment of the inertia weight further improved convergence efficiency and enhanced global optimization performance.In addition,a Cauchy-Gaussian hybrid update strategy was incorporated to inject randomness and variation into the search process,which helped the algorithm escape local minima and maintain a high level of solution diversity.This approach significantly enhanced the robustness and adaptability of the optimization process.Simulation experiments confirmed that the improved SSA consistently outperformed benchmark algorithms such as the original SSA,PSO,and WMR-SSA.Compared with the three algorithms,in the simulated sea area,the path lengths of the proposed algorithm are reduced by 21%,21%,and 16%,respectively,and under the actual sea simulation conditions,the path lengths are reduced by 13%,15%,and 11%,respectively.The results highlighted the effectiveness and practicality of the proposed method,providing an effective solution for intelligent and autonomous USV navigation in complex ocean environments.
基金supported by the National Social Science Fund of China(2022-SKJJ-B-084).
文摘The learning algorithms of causal discovery mainly include score-based methods and genetic algorithms(GA).The score-based algorithms are prone to searching space explosion.Classical GA is slow to converge,and prone to falling into local optima.To address these issues,an improved GA with domain knowledge(IGADK)is proposed.Firstly,domain knowledge is incorporated into the learning process of causality to construct a new fitness function.Secondly,a dynamical mutation operator is introduced in the algorithm to accelerate the convergence rate.Finally,an experiment is conducted on simulation data,which compares the classical GA with IGADK with domain knowledge of varying accuracy.The IGADK can greatly reduce the number of iterations,populations,and samples required for learning,which illustrates the efficiency and effectiveness of the proposed algorithm.
文摘Background:The Functional Movement Screen(FMS^(TM)) has become increasingly popular for identifying functional limitations in basic functional movements.This exploratory and descriptive study was undertaken to confirm feasibility of performing the FMS^(TM) in older active adults,assess prevalence of asymmetries and to evaluate the relationship between functional movement ability,age,physical activity levels and body mass index(BMI).Methods:This is an observational study;97 men(n = 53) and women(n = 44) between the ages of 52 and 83 participated.BMI was computed and self-reported physical activity levels were obtained.Subjects were grouped by age(5-year intervals),BMI(normal,over-weight,and obese)and sex.Each participant's performance on the FMS^(TM) was digitally recorded for later analysis.Results:The youngest age group(50–54 years) scored highest in all seven tests and the oldest age group(75+) scored lowest in most of the tests compared to all other age groups.The subjects in the 'normal weight' group performed no different than those who were in the 'overweight' group;both groups performed better than the 'obese' group.Of the 97 participants 54 had at least one asymmetry.The pairwise correlations between the total FMS^(TM) score and age(r =-0.531),BMI(r =-0.270),and the measure of activity level(r = 0.287) were significant(p < 0.01 for all).Conclusion:FMS^(TM) scores decline with increased BMI,increased age,and decreased activity level.The screen identifies range of motion-and strength-related asymmetries.The FMS^(TM) can be used to assess functional limitations and asymmetries.Future research should evaluate if a higher total FMS^(TM) score is related to fewer falls or injuries in the older population.
文摘Forecasting crop yields based on remote sensing data is one of the most important tasks in agriculture.Soybean is the main crop in the Russian Far East.It is desirable to forecast soybean yield as early as possible while maintaining high accuracy.This study aimed to investigate seasonal time series of the normalized difference vegetation index(NDVI) to achieve early forecasting of soybean yield.This research used data from the Moderate Resolution Image Spectroradiometer(MODIS),an arable-land mask obtained from the VEGA-Science web service,and soybean yield data for 2008-2017 for the Jewish Autonomous Region(JAR) districts.Four approximating functions were fitted to model the NDVI time series:Gaussian,double logistic(DL),and quadratic and cubic polynomials.In the period from calendar weeks 22-42(end of May to mid-October),averaged over two districts,the model using the DL function showed the highest accuracy(mean absolute percentage error-4.0%,root mean square error(RMSE)-0.029,P <0.01).The yield forecast accuracy of prediction in the period of weeks 25-30 in JAR municipalities using the parameters of the Gaussian function was higher(P <0.05) than that using the other functions.The mean forecast error for the Gaussian function was 14.9% in week 25(RMSE was0.21 t ha) and 5.1%-12.9% in weeks 26-30(RMSE varied from 0.06 to 0.15 t ha) according to the2013-2017 data.In weeks 31-32,the error was 5.0%-5.4%(RMSE was 0.07 t ha) using the Gaussian parameters and 7.4%-7.7%(RMSE was 0.09-0.11 t ha) for the DL function.When the method was applied to municipal districts of other soy-producing regions of the Russian Far East.RMSE was0.14-0.32 t hain weeks 25-26 and did not exceed 0.20 t hain subsequent weeks.
基金sponsored by the Annual Earthquake Tracking Task,CEA(2017010214)
文摘Based on the existing continuous borehole strain observation,the multiquadric function fitting method was used to deal with time series data. The impact of difference kernel function parameters was discussed to obtain a valuable fitting result,from which the physical connotation of the original data and its possible applications were analyzed.Meanwhile,a brief comparison was made between the results of multiquadric function fitting and polynomial fitting.
文摘The aim of this study was to determine differences in body composition and physical fitness in elderly men and women. Five hundred twenty six subjects were included in this study, 272 were men （52%） and 254 women （48%）. To determine the trend of changes of anthropometric parameters and physical fitness in people aged over 60, the authors were divided subjects in 5 age groups： 60-64, 65-69, 70-74, 75-79 and over 80 years of age. Decrease in strength is observed with the aging process so that the respondents aged 60-64 years significantly differ in the strength of the lower extremities of elderly subjects 70-74 and 75-79 years of age. Also this difference is noticeable if they compare with men and women. In this study, they found that there was an increase in the amount of adipose tissue, reducing the level of muscle activity and decreased muscle strength and endurance of the aging process.
基金supported by the National Natural Science Foundation of China (60873069 61171132)+3 种基金the Jiangsu Government Scholarship for Overseas Studies (JS-2010-K005)the Funding of Jiangsu Innovation Program for Graduate Education (CXZZ11 0219)the Open Project Program of Jiangsu Provincial Key Laboratory of Computer Information Processing Technology (KJS1023)the Applying Study Foundation of Nantong (BK2011062)
文摘Particle swarm optimization （PSO） is a new heuristic algorithm which has been applied to many optimization problems successfully. Attribute reduction is a key studying point of the rough set theory, and it has been proven that computing minimal reduc- tion of decision tables is a non-derterministic polynomial （NP）-hard problem. A new cooperative extended attribute reduction algorithm named Co-PSAR based on improved PSO is proposed, in which the cooperative evolutionary strategy with suitable fitness func- tions is involved to learn a good hypothesis for accelerating the optimization of searching minimal attribute reduction. Experiments on Benchmark functions and University of California, Irvine （UCI） data sets, compared with other algorithms, verify the superiority of the Co-PSAR algorithm in terms of the convergence speed, efficiency and accuracy for the attribute reduction.
文摘Based on the bat algorithm(BA), this paper proposes a discrete BA(DBA) approach to optimize the disassembly sequence planning(DSP) problem, for the purpose of obtaining an optimum disassembly sequence(ODS) of a product with a high degree of automation and guiding maintenance operation. The BA for solving continuous problems is introduced, and combining with mathematical formulations, the BA is reformed to be the DBA for DSP problems. The fitness function model(FFM) is built to evaluate the quality of disassembly sequences. The optimization performance of the DBA is tested and verified by an application case, and the DBA is compared with the genetic algorithm(GA), particle swarm optimization(PSO) algorithm and differential mutation BA(DMBA). Numerical experiments show that the proposed DBA has a better optimization capability and provides more accurate solutions than the other three algorithms.
文摘Particle filter(PF) can solve the problem of state estimation under strong non-linear non-Gaussian noise condition with respect to traditional Kalman filter(KF) and those improved KFs such as extended KF(EKF) and unscented KF(UKF). However, problems such as particle depletion and particle degradation affect the performance of PF. Optimizing the particle set to high likelihood region with intelligent optimization algorithm results in a more reasonable distribution of the sampling particles and more accurate state estimation. In this paper, a novel bird swarm algorithm based PF(BSAPF) is presented. Firstly, different behavior models are established by emulating the predation, flight, vigilance and follower behavior of the birds. Then, the observation information is introduced into the optimization process of the proposal distribution with the design of fitness function. In order to prevent particles from getting premature(being stuck into local optimum) and increase the diversity of particles, Lévy flight is designed to increase the randomness of particle's movement. Finally,the proposed algorithm is applied to estimate the speed of the train under the condition that the measurement noise of the wheel sensor is non-Gaussian distribution. Simulation study and experimental results both show that BSAPF is more accurate and has more effective particle number as compared with PF and UKF, demonstrating the promising performance of the method.
基金Project supported by the National Natural Science Foundation of China (No. 60105003) and the Natural Science Foundation of Zhejiang Province (No. 600025), China
文摘Mobile robot global path planning in a static environment is an important problem. The paper proposes a method of global path planning based on neural network and genetic algorithm. We constructed the neural network model of environmental information in the workspace for a robot and used this model to establish the relationship between a collision avoidance path and the output of the model. Then the two-dimensional coding for the path via-points was converted to one-dimensional one and the fitness of both the collision avoidance path and the shortest distance are integrated into a fitness function. The simulation results showed that the proposed method is correct and effective.
文摘Recently,vehicular ad hoc networks(VANETs)finds applicability in different domains such as security,rescue operations,intelligent transportation systems(ITS),etc.VANET has unique features like high mobility,limited mobility patterns,adequate topologymodifications,and wireless communication.Despite the benefits of VANET,scalability is a challenging issue which could be addressed by the use of cluster-based routing techniques.It enables the vehicles to perform intercluster communication via chosen CHs and optimal routes.The main drawback of VANET network is the network unsteadiness that results in minimum lifetime.In order to avoid reduced network lifetime in VANET,this paper presents an enhanced metaheuristics based clustering with multihop routing technique for lifetime maximization(EMCMHR-LM)in VANET.The presented EMCMHR-LM model involves the procedure of arranging clusters,cluster head(CH)selection,and route selection appropriate for VANETs.The presentedEMCMHR-LMmodel uses slime mold optimization based clustering(SMO-C)technique to group the vehicles into clusters.Besides,an enhanced wild horse optimization based multihop routing(EWHO-MHR)protocol by the optimization of network parameters.The presented EMCMHR-LMmodel is simulated usingNetwork Simulator(NS3)tool and the simulation outcomes reported the enhanced performance of the proposed EMCMHR-LM technique over the other models.
基金Project(2009CB320603)supported by the National Basic Research Program of ChinaProject(IRT0712)supported by Program for Changjiang Scholars and Innovative Research Team in University+1 种基金Project(B504)supported by the Shanghai Leading Academic Discipline ProgramProject(61174118)supported by the National Natural Science Foundation of China
文摘In order to reduce the computation of complex problems, a new surrogate-assisted estimation of distribution algorithm with Gaussian process was proposed. Coevolution was used in dual populations which evolved in parallel. The search space was projected into multiple subspaces and searched by sub-populations. Also, the whole space was exploited by the other population which exchanges information with the sub-populations. In order to make the evolutionary course efficient, multivariate Gaussian model and Gaussian mixture model were used in both populations separately to estimate the distribution of individuals and reproduce new generations. For the surrogate model, Gaussian process was combined with the algorithm which predicted variance of the predictions. The results on six benchmark functions show that the new algorithm performs better than other surrogate-model based algorithms and the computation complexity is only 10% of the original estimation of distribution algorithm.
基金supported by the National Natural Science Foundation of China(No.60803049,60472060)
文摘In many real-world applications of evolutionary algorithms,the fitness of an individual requires a quantitative measure.This paper proposes a self-adaptive linear evolutionary algorithm （ALEA） in which we introduce a novel strategy for evaluating individual＇s relative strengths and weaknesses.Based on this strategy,searching space of constrained optimization problems with high dimensions for design variables is compressed into two-dimensional performance space in which it is possible to quickly identify ＇good＇ individuals of the performance for a multiobjective optimization application,regardless of original space complexity.This is considered as our main contribution.In addition,the proposed new evolutionary algorithm combines two basic operators with modification in reproduction phase,namely,crossover and mutation.Simulation results over a comprehensive set of benchmark functions show that the proposed strategy is feasible and effective,and provides good performance in terms of uniformity and diversity of solutions.
文摘The three-layer forward neural networks are used to establish the inverse kinematics models of robot manipulators. The fuzzy genetic algorithm based on the linear scaling of the fitness value is presented to update the weights of neural networks. To increase the search speed of the algorithm, the crossover probability and the mutation probability are adjusted through fuzzy control and the fitness is modified by the linear scaling method in FGA. Simulations show that the proposed method improves considerably the precision of the inverse kinematics solutions for robot manipulators and guarantees a rapid global convergence and overcomes the drawbacks of SGA and the BP algorithm.
基金Supported by the National Natural Science Foundation of China （No. 60302020）.
文摘All the parameters of beamforming are usually optimized simultaneously in implementing the optimization of antenna array pattern with multiple objectives and parameters by genetic algorithms （GAs）. Firstly, this paper analyzes the performance of fitness functions of previous algorithms. It shows that original algorithms make the fitness functions too complex leading to large amount of calculation, and also the selection of the weight of parameters very sensitive due to many parameters optimized simultaneously. This paper proposes a kind of algorithm of composite beamforming, which detaches the antenna array into two parts corresponding to optimization of different objective parameters respectively. New algorithm substitutes the previous complex fitness function with two simpler functions. Both theoretical analysis and simulation results show that this method simplifies the selection of weighting parameters and reduces the complexity of calculation. Furthermore, the algorithm has better performance in lowering side lobe and interferences in comparison with conventional algorithms of beamforming in the case of slightly widening the main lobe.
基金This work was supported by National Key R&D Program of China(Grant No.2017YFB0802000)National Natural Science Foundation of China(Grant No.U1636114,61772550,61572521)National Cryptography Development Fund of China(Grant No.MMJJ20170112).
文摘As one of the typical method for side channel attack,DPA has become a serious trouble for the security of encryption algorithm implementation.The potential capability of DPA attack induces researchers making a lot of efforts in this area,which significantly improved the attack efficiency of DPA.However,most of these efforts were made based on the hypothesis that the gathered power consumption data from the target device were stable and low noise.If large deviation happens in part of the power consumption data sample,the efficiency of DPA attack will be reduced rapidly.In this work,a highly efficient method for DPA attack is proposed with the inspiration of genetic algorithm.Based on the designed fitness function,power consumption data that is stable and less noisy will be selected and the noisy ones will be eliminated.In this way,not only improves the robustness and efficiency of DPA attack,but also reduces the number of samples needed.With experiments on block cipher algorithms of DES and SM4,10%and 12.5%of the number of power consumption curves have been reduced in average with the proposed DPAG algorithm compared to original DPA attack respectively.The high efficiency and correctness of the proposed algorithm and novel model are proved by experiments.
文摘In order to solve the problem between searching performance and convergence of genetic algorithms, a fast genetic algorithm generalized self-adaptive genetic algorithm (GSAGA) is presented. (1) Evenly distributed initial population is generated. (2) Superior individuals are not broken because of crossover and mutation operation for they are sent to subgeneration directly. (3) High quality im- migrants are introduced according to the condition of the population schema. (4) Crossover and mutation are operated on self-adaptation. Therefore, GSAGA solves the coordination problem between convergence and searching performance. In GSAGA, the searching per- formance and global convergence are greatly improved compared with many existing genetic algorithms. Through simulation, the val- idity of this modified genetic algorithm is proved.
基金This research was supported by Hankuk University of Foreign Studies Research Fund of 2021.Also,This research was supported by the MIST(Ministry of Science,ICT),Korea,under the National Program for Excellence in SW),supervised by the IITP(Institute of Information&communications Technology Planing&Evaluation)in 2021”(2019-0-01816).
文摘In recent times,internet of things(IoT)applications on the cloud might not be the effective solution for every IoT scenario,particularly for time sensitive applications.A significant alternative to use is edge computing that resolves the problem of requiring high bandwidth by end devices.Edge computing is considered a method of forwarding the processing and communication resources in the cloud towards the edge.One of the considerations of the edge computing environment is resource management that involves resource scheduling,load balancing,task scheduling,and quality of service(QoS)to accomplish improved performance.With this motivation,this paper presents new soft computing based metaheuristic algorithms for resource scheduling(RS)in the edge computing environment.The SCBMARS model involves the hybridization of the Group Teaching Optimization Algorithm(GTOA)with rat swarm optimizer(RSO)algorithm for optimal resource allocation.The goal of the SCBMA-RS model is to identify and allocate resources to every incoming user request in such a way,that the client’s necessities are satisfied with the minimum number of possible resources and optimal energy consumption.The problem is formulated based on the availability of VMs,task characteristics,and queue dynamics.The integration of GTOA and RSO algorithms assist to improve the allocation of resources among VMs in the data center.For experimental validation,a comprehensive set of simulations were performed using the CloudSim tool.The experimental results showcased the superior performance of the SCBMA-RS model interms of different measures.
文摘The nodes in the sensor network have a wide range of uses,particularly on under-sea links that are skilled for detecting,handling as well as management.The underwater wireless sensor networks support collecting pollution data,mine survey,oceanographic information collection,aided navigation,strategic surveillance,and collection of ocean samples using detectors that are submerged inwater.Localization,congestion routing,and prioritizing the traffic is the major issue in an underwater sensor network.Our scheme differentiates the different types of traffic and gives every type of traffic its requirements which is considered regarding network resource.Minimization of localization error using the proposed angle-based forwarding scheme is explained in this paper.We choose the shortest path to the destination using the fitness function which is calculated based on fault ratio,dispatching of packets,power,and distance among the nodes.This work contemplates congestion conscious forwarding using hard stage and soft stage schemes which reduce the congestion by monitoring the status of the energy and buffer of the nodes and controlling the traffic.The study with the use of the ns3 simulator demonstrated that a given algorithm accomplishes superior performance for loss of packet,delay of latency,and power utilization than the existing algorithms.
