In the process of identifying parameters for a permanent magnet synchronous motor,the particle swarm optimization method is prone to being stuck in local optima in the later stages of iteration,resulting in low parame...In the process of identifying parameters for a permanent magnet synchronous motor,the particle swarm optimization method is prone to being stuck in local optima in the later stages of iteration,resulting in low parameter accuracy.This work proposes a fuzzy particle swarm optimization approach based on the transformation function and the filled function.This approach addresses the topic of particle swarmoptimization in parameter identification from two perspectives.Firstly,the algorithm uses a transformation function to change the form of the fitness function without changing the position of the extreme point of the fitness function,making the extreme point of the fitness function more prominent and improving the algorithm’s search ability while reducing the algorithm’s computational burden.Secondly,on the basis of themulti-loop fuzzy control systembased onmultiplemembership functions,it is merged with the filled function to improve the algorithm’s capacity to skip out of the local optimal solution.This approach can be used to identify the parameters of permanent magnet synchronous motors by sampling only the stator current,voltage,and speed data.The simulation results show that the method can effectively identify the electrical parameters of a permanent magnet synchronous motor,and it has superior global convergence performance and robustness.展开更多
Particle Swarm Optimization(PSO)has been utilized as a useful tool for solving intricate optimization problems for various applications in different fields.This paper attempts to carry out an update on PSO and gives a...Particle Swarm Optimization(PSO)has been utilized as a useful tool for solving intricate optimization problems for various applications in different fields.This paper attempts to carry out an update on PSO and gives a review of its recent developments and applications,but also provides arguments for its efficacy in resolving optimization problems in comparison with other algorithms.Covering six strategic areas,which include Data Mining,Machine Learning,Engineering Design,Energy Systems,Healthcare,and Robotics,the study demonstrates the versatility and effectiveness of the PSO.Experimental results are,however,used to show the strong and weak parts of PSO,and performance results are included in tables for ease of comparison.The results stress PSO’s efficiency in providing optimal solutions but also show that there are aspects that need to be improved through combination with algorithms or tuning to the parameters of the method.The review of the advantages and limitations of PSO is intended to provide academics and practitioners with a well-rounded view of the methods of employing such a tool most effectively and to encourage optimized designs of PSO in solving theoretical and practical problems in the future.展开更多
The constriction factor method (CFM) is a new variation of the basic particle swarm optimization (PSO), which has relatively better convergent nature. The effects of the major parameters on CFM were systematically inv...The constriction factor method (CFM) is a new variation of the basic particle swarm optimization (PSO), which has relatively better convergent nature. The effects of the major parameters on CFM were systematically investigated based on some benchmark functions. The constriction factor, velocity constraint, and population size all have significant impact on the per- formance of CFM for PSO. The constriction factor and velocity constraint have optimal values in practical application, and im- proper choice of these factors will lead to bad results. Increasing population size can improve the solution quality, although the computing time will be longer. The characteristics of CFM parameters are described and guidelines for determining parameter values are given in this paper.展开更多
This paper proposes a novel quantum-behaved particle swarm optimization （NQPSO） for the estimation of chaos＇ unknown parameters by transforming them into nonlinear functions＇ optimization. By means of the techniqu...This paper proposes a novel quantum-behaved particle swarm optimization （NQPSO） for the estimation of chaos＇ unknown parameters by transforming them into nonlinear functions＇ optimization. By means of the techniques in the following three aspects： contracting the searching space self-adaptively; boundaries restriction strategy; substituting the particles＇ convex combination for their centre of mass, this paper achieves a quite effective search mechanism with fine equilibrium between exploitation and exploration. Details of applying the proposed method and other methods into Lorenz systems are given, and experiments done show that NQPSO has better adaptability, dependability and robustness. It is a successful approach in unknown parameter estimation online especially in the cases with white noises.展开更多
The future Sixth-Generation (6G) wireless systems are expected to encounter emerging services with diverserequirements. In this paper, 6G network resource orchestration is optimized to support customized networkslicin...The future Sixth-Generation (6G) wireless systems are expected to encounter emerging services with diverserequirements. In this paper, 6G network resource orchestration is optimized to support customized networkslicing of services, and place network functions generated by heterogeneous devices into available resources.This is a combinatorial optimization problem that is solved by developing a Particle Swarm Optimization (PSO)based scheduling strategy with enhanced inertia weight, particle variation, and nonlinear learning factor, therebybalancing the local and global solutions and improving the convergence speed to globally near-optimal solutions.Simulations show that the method improves the convergence speed and the utilization of network resourcescompared with other variants of PSO.展开更多
Hyperparameter optimization is considered as one of the most challenges in deep learning and dominates the precision of model in a certain.Recent proposals tried to solve this issue through the particle swarm optimiza...Hyperparameter optimization is considered as one of the most challenges in deep learning and dominates the precision of model in a certain.Recent proposals tried to solve this issue through the particle swarm optimization(PSO),but its native defect may result in the local optima trapped and convergence difficulty.In this paper,the genetic operations are introduced to the PSO,which makes the best hyperparameter combination scheme for specific network architecture be located easier.Spe-cifically,to prevent the troubles caused by the different data types and value scopes,a mixed coding method is used to ensure the effectiveness of particles.Moreover,the crossover and mutation opera-tions are added to the process of particles updating,to increase the diversity of particles and avoid local optima in searching.Verified with three benchmark datasets,MNIST,Fashion-MNIST,and CIFAR10,it is demonstrated that the proposed scheme can achieve accuracies of 99.58%,93.39%,and 78.96%,respectively,improving the accuracy by about 0.1%,0.5%,and 2%,respectively,compared with that of the PSO.展开更多
Feature selection(FS)is a data preprocessing step in machine learning(ML)that selects a subset of relevant and informative features from a large feature pool.FS helps ML models improve their predictive accuracy at low...Feature selection(FS)is a data preprocessing step in machine learning(ML)that selects a subset of relevant and informative features from a large feature pool.FS helps ML models improve their predictive accuracy at lower computational costs.Moreover,FS can handle the model overfitting problem on a high-dimensional dataset.A major problem with the filter and wrapper FS methods is that they consume a significant amount of time during FS on high-dimensional datasets.The proposed“HDFS(PSO-MI):hybrid distribute feature selection using particle swarm optimization-mutual information(PSO-MI)”,is a PSO-based hybrid method that can overcome the problem mentioned above.This method hybridizes the filter and wrapper techniques in a distributed manner.A new combiner is also introduced to merge the effective features selected from multiple data distributions.The effectiveness of the proposed HDFS(PSO-MI)method is evaluated using five ML classifiers,i.e.,logistic regression(LR),k-NN,support vector machine(SVM),decision tree(DT),and random forest(RF),on various datasets in terms of accuracy and Matthew’s correlation coefficient(MCC).From the experimental analysis,we observed that HDFS(PSO-MI)method yielded more than 98%,95%,92%,90%,and 85%accuracy for the unbalanced,kidney disease,emotions,wafer manufacturing,and breast cancer datasets,respectively.Our method shows promising results comapred to other methods,such as mutual information,gain ratio,Spearman correlation,analysis of variance(ANOVA),Pearson correlation,and an ensemble feature selection with ranking method(EFSRank).展开更多
Updating the velocity in particle swarm optimization (PSO) consists of three terms: the inertia term, the cognitive term and the social term. The balance of these terms determines the balance of the global and local s...Updating the velocity in particle swarm optimization (PSO) consists of three terms: the inertia term, the cognitive term and the social term. The balance of these terms determines the balance of the global and local search abilities, and therefore the performance of PSO. In this work, an adaptive parallel PSO algorithm, which is based on the dynamic exchange of control parameters between adjacent swarms, has been developed. The proposed PSO algorithm enables us to adaptively optimize inertia factors, learning factors and swarm activity. By performing simulations of a search for the global minimum of a benchmark multimodal function, we have found that the proposed PSO successfully provides appropriate control parameter values, and thus good global optimization performance.展开更多
Particle swarm optimization (PSO) is an efficient, robust and simple optimization algorithm. Most studies are mainly concentrated on better understanding of the standard PSO control parameters, such as acceleration co...Particle swarm optimization (PSO) is an efficient, robust and simple optimization algorithm. Most studies are mainly concentrated on better understanding of the standard PSO control parameters, such as acceleration coefficients, etc. In this paper, a more simple strategy of PSO algorithm called θ-PSO is proposed. In θ-PSO, an increment of phase angle vector replaces the increment of velocity vector and the positions are decided by the mapping of phase angles. Benchmark testing of nonlinear func- tions is described and the results show that the performance of θ-PSO is much more effective than that of the standard PSO.展开更多
Particle swarm optimization （PSO）, like other evolutionary algorithms is a population-based stochastic algorithm inspired from the metaphor of social interaction in birds, insects, wasps, etc. It has been used for f...Particle swarm optimization （PSO）, like other evolutionary algorithms is a population-based stochastic algorithm inspired from the metaphor of social interaction in birds, insects, wasps, etc. It has been used for finding promising solutions in complex search space through the interaction of particles in a swarm. It is a well recognized fact that the performance of evolutionary algorithms to a great extent depends on the choice of appropriate strategy/operating parameters like population size, crossover rate, mutation rate, crossover operator, etc. Generally, these parameters are selected through hit and trial process, which is very unsystematic and requires rigorous experimentation. This paper proposes a systematic based on Taguchi method reasoning scheme for rapidly identifying the strategy parameters for the PSO algorithm. The Taguchi method is a robust design approach using fractional factorial design to study a large number of parameters with small number of experiments. Computer simulations have been performed on two benchmark functionsiRosenbrock function and Griewank functionito validate the approach.展开更多
In order to improve the accuracy and reliability of the driving fatigue detection based on a single feature, a new detection algorithm based on multiple features is proposed. Two direct driver＇s facial features refle...In order to improve the accuracy and reliability of the driving fatigue detection based on a single feature, a new detection algorithm based on multiple features is proposed. Two direct driver＇s facial features reflecting fatigue and one indirect vehicle behavior feature indicating fatigue are considered. Meanwhile, T-S fuzzy neural network（TSFNN）is adopted to recognize the driving fatigue of drivers. For the structure identification of the TSFNN, subtractive clustering（SC） is used to confirm the fuzzy rules and their correlative parameters. Moreover, the particle swarm optimization （PSO）algorithm is improved to train the TSFNN. Simulation results and experiments on vehicles show that the proposed algorithm can effectively improve the convergence speed and the recognition accuracy of the TSFNN, as well as enhance the correct rate of driving fatigue detection.展开更多
In shock wave＇s pressure testing,a dynamic compensation digital filter is designed based on particle swarm optimization （PSO） algorithm.Dynamic calibration experiment and simulation are conducted for the pressure s...In shock wave＇s pressure testing,a dynamic compensation digital filter is designed based on particle swarm optimization （PSO） algorithm.Dynamic calibration experiment and simulation are conducted for the pressure sensor.PSO algorithm is applied on Matlab platform to achieve optimization according to input and output data of the sensor as well as the reference model,and the global optimal values got by optimization become the parameters of the compensator.Finally,the dynamic compensation filter is established on LabVIEW platform.The experimental results show that the data after processing with the compensation filter truly reflects the input signal.展开更多
The common failure mechanism for brittle rocks is known to be axial splitting which happens parallel to the direction of maximum compression. One of the mechanisms proposed for modelling of axial splitting is the slid...The common failure mechanism for brittle rocks is known to be axial splitting which happens parallel to the direction of maximum compression. One of the mechanisms proposed for modelling of axial splitting is the sliding crack or so called, “wing crack” model. Fairhurst-Cook model explains this specific type of failure which starts by a pre-crack and finally breaks the rock by propagating 2-D cracks under uniaxial compression. In this paper, optimization of this model has been considered and the process has been done by a complete sensitivity analysis on the main parameters of the model and excluding the trends of their changes and also their limits and “peak points”. Later on this paper, three artificial intelligence algorithms including Particle Swarm Intelligence (PSO), Ant Colony Optimization (ACO) and genetic algorithm (GA) has been used and compared in order to achieve optimized sets of parameters resulting in near-maximum or near-minimum amounts of wedging forces creating a wing crack.展开更多
High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation lear...High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation learning to an HDI matrix,whose hyper-parameter adaptation can be implemented through a particle swarm optimizer(PSO) to meet scalable requirements.However, conventional PSO is limited by its premature issues,which leads to the accuracy loss of a resultant LFA model. To address this thorny issue, this study merges the information of each particle's state migration into its evolution process following the principle of a generalized momentum method for improving its search ability, thereby building a state-migration particle swarm optimizer(SPSO), whose theoretical convergence is rigorously proved in this study. It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss. Experiments on six HDI matrices indicate that an SPSO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational efficiency.Hence, SPSO's use ensures efficient and reliable hyper-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices.展开更多
For multi-objective optimization problems, particle swarm optimization(PSO) algorithm generally needs a large number of fitness evaluations to obtain the Pareto optimal solutions. However, it will become substantially...For multi-objective optimization problems, particle swarm optimization(PSO) algorithm generally needs a large number of fitness evaluations to obtain the Pareto optimal solutions. However, it will become substantially time-consuming when handling computationally expensive fitness functions. In order to save the computational cost, a surrogate-assisted PSO with Pareto active learning is proposed. In real physical space(the objective functions are computationally expensive), PSO is used as an optimizer, and its optimization results are used to construct the surrogate models. In virtual space, objective functions are replaced by the cheaper surrogate models, PSO is viewed as a sampler to produce the candidate solutions. To enhance the quality of candidate solutions, a hybrid mutation sampling method based on the simulated evolution is proposed, which combines the advantage of fast convergence of PSO and implements mutation to increase diversity. Furthermore, ε-Pareto active learning(ε-PAL)method is employed to pre-select candidate solutions to guide PSO in the real physical space. However, little work has considered the method of determining parameter ε. Therefore, a greedy search method is presented to determine the value ofεwhere the number of active sampling is employed as the evaluation criteria of classification cost. Experimental studies involving application on a number of benchmark test problems and parameter determination for multi-input multi-output least squares support vector machines(MLSSVM) are given, in which the results demonstrate promising performance of the proposed algorithm compared with other representative multi-objective particle swarm optimization(MOPSO) algorithms.展开更多
As for the drop of particle diversity and the slow convergent speed of particle in the late evolution period when particle swarm optimization（PSO） is applied to solve high-dimensional multi-modal functions,a hybrid ...As for the drop of particle diversity and the slow convergent speed of particle in the late evolution period when particle swarm optimization（PSO） is applied to solve high-dimensional multi-modal functions,a hybrid optimization algorithm based on the cat mapping,the cloud model and PSO is proposed.While the PSO algorithm evolves a certain of generations,this algorithm applies the cat mapping to implement global disturbance of the poorer individuals,and employs the cloud model to execute local search of the better individuals;accordingly,the obtained best individuals form a new swarm.For this new swarm,the evolution operation is maintained with the PSO algorithm,using the parameter of pop distr to balance the global and local search capacity of the algorithm,as well as,adopting the parameter of mix gen to control mixing times of the algorithm.The comparative analysis is carried out on the basis of 4 functions and other algorithms.It indicates that this algorithm shows faster convergent speed and better solving precision for solving functions particularly those high-dimensional multi-modal functions.Finally,the suggested values are proposed for parameters pop distr and mix gen applied to different dimension functions via the comparative analysis of parameters.展开更多
This paper proposes an artificial neural network(ANN) based software reliability model trained by novel particle swarm optimization(PSO) algorithm for enhanced forecasting of the reliability of software. The proposed ...This paper proposes an artificial neural network(ANN) based software reliability model trained by novel particle swarm optimization(PSO) algorithm for enhanced forecasting of the reliability of software. The proposed ANN is developed considering the fault generation phenomenon during software testing with the fault complexity of different levels. We demonstrate the proposed model considering three types of faults residing in the software. We propose a neighborhood based fuzzy PSO algorithm for competent learning of the proposed ANN using software failure data. Fitting and prediction performances of the neighborhood fuzzy PSO based proposed neural network model are compared with the standard PSO based proposed neural network model and existing ANN based software reliability models in the literature through three real software failure data sets. We also compare the performance of the proposed PSO algorithm with the standard PSO algorithm through learning of the proposed ANN. Statistical analysis shows that the neighborhood fuzzy PSO based proposed neural network model has comparatively better fitting and predictive ability than the standard PSO based proposed neural network model and other ANN based software reliability models. Faster release of software is achievable by applying the proposed PSO based neural network model during the testing period.展开更多
An improved particle swarm optimization（PSO） algorithm is proposed to train the fuzzy support vector machine（FSVM） for pattern multi-classification.In the improved algorithm,the particles studies not only from its...An improved particle swarm optimization（PSO） algorithm is proposed to train the fuzzy support vector machine（FSVM） for pattern multi-classification.In the improved algorithm,the particles studies not only from itself and the best one but also from the mean value of some other particles.In addition,adaptive mutation was introduced to reduce the rate of premature convergence.The experimental results on the synthetic aperture radar（SAR） target recognition of moving and stationary target acquisition and recognition（MSTAR） dataset and character recognition of MNIST database show that the improved algorithm is feasible and effective for fuzzy multi-class SVM training.展开更多
Interval type-2 fuzzy neural networks(IT2FNNs)can be seen as the hybridization of interval type-2 fuzzy systems(IT2FSs) and neural networks(NNs). Thus, they naturally inherit the merits of both IT2 FSs and NNs. Althou...Interval type-2 fuzzy neural networks(IT2FNNs)can be seen as the hybridization of interval type-2 fuzzy systems(IT2FSs) and neural networks(NNs). Thus, they naturally inherit the merits of both IT2 FSs and NNs. Although IT2 FNNs have more advantages in processing uncertain, incomplete, or imprecise information compared to their type-1 counterparts, a large number of parameters need to be tuned in the IT2 FNNs,which increases the difficulties of their design. In this paper,big bang-big crunch(BBBC) optimization and particle swarm optimization(PSO) are applied in the parameter optimization for Takagi-Sugeno-Kang(TSK) type IT2 FNNs. The employment of the BBBC and PSO strategies can eliminate the need of backpropagation computation. The computing problem is converted to a simple feed-forward IT2 FNNs learning. The adoption of the BBBC or the PSO will not only simplify the design of the IT2 FNNs, but will also increase identification accuracy when compared with present methods. The proposed optimization based strategies are tested with three types of interval type-2 fuzzy membership functions(IT2FMFs) and deployed on three typical identification models. Simulation results certify the effectiveness of the proposed parameter optimization methods for the IT2 FNNs.展开更多
基金the Natural Science Foundation of China under Grant 52077027in part by the Liaoning Province Science and Technology Major Project No.2020JH1/10100020.
文摘In the process of identifying parameters for a permanent magnet synchronous motor,the particle swarm optimization method is prone to being stuck in local optima in the later stages of iteration,resulting in low parameter accuracy.This work proposes a fuzzy particle swarm optimization approach based on the transformation function and the filled function.This approach addresses the topic of particle swarmoptimization in parameter identification from two perspectives.Firstly,the algorithm uses a transformation function to change the form of the fitness function without changing the position of the extreme point of the fitness function,making the extreme point of the fitness function more prominent and improving the algorithm’s search ability while reducing the algorithm’s computational burden.Secondly,on the basis of themulti-loop fuzzy control systembased onmultiplemembership functions,it is merged with the filled function to improve the algorithm’s capacity to skip out of the local optimal solution.This approach can be used to identify the parameters of permanent magnet synchronous motors by sampling only the stator current,voltage,and speed data.The simulation results show that the method can effectively identify the electrical parameters of a permanent magnet synchronous motor,and it has superior global convergence performance and robustness.
文摘Particle Swarm Optimization(PSO)has been utilized as a useful tool for solving intricate optimization problems for various applications in different fields.This paper attempts to carry out an update on PSO and gives a review of its recent developments and applications,but also provides arguments for its efficacy in resolving optimization problems in comparison with other algorithms.Covering six strategic areas,which include Data Mining,Machine Learning,Engineering Design,Energy Systems,Healthcare,and Robotics,the study demonstrates the versatility and effectiveness of the PSO.Experimental results are,however,used to show the strong and weak parts of PSO,and performance results are included in tables for ease of comparison.The results stress PSO’s efficiency in providing optimal solutions but also show that there are aspects that need to be improved through combination with algorithms or tuning to the parameters of the method.The review of the advantages and limitations of PSO is intended to provide academics and practitioners with a well-rounded view of the methods of employing such a tool most effectively and to encourage optimized designs of PSO in solving theoretical and practical problems in the future.
基金Project (No. 20276063) supported by the National Natural Sci-ence Foundation of China
文摘The constriction factor method (CFM) is a new variation of the basic particle swarm optimization (PSO), which has relatively better convergent nature. The effects of the major parameters on CFM were systematically investigated based on some benchmark functions. The constriction factor, velocity constraint, and population size all have significant impact on the per- formance of CFM for PSO. The constriction factor and velocity constraint have optimal values in practical application, and im- proper choice of these factors will lead to bad results. Increasing population size can improve the solution quality, although the computing time will be longer. The characteristics of CFM parameters are described and guidelines for determining parameter values are given in this paper.
基金Project supported by the National Natural Science Foundation of China (Grant No 10647141)
文摘This paper proposes a novel quantum-behaved particle swarm optimization （NQPSO） for the estimation of chaos＇ unknown parameters by transforming them into nonlinear functions＇ optimization. By means of the techniques in the following three aspects： contracting the searching space self-adaptively; boundaries restriction strategy; substituting the particles＇ convex combination for their centre of mass, this paper achieves a quite effective search mechanism with fine equilibrium between exploitation and exploration. Details of applying the proposed method and other methods into Lorenz systems are given, and experiments done show that NQPSO has better adaptability, dependability and robustness. It is a successful approach in unknown parameter estimation online especially in the cases with white noises.
基金supported by the Social Scientific Research Foundation of China(21VSZ126).
文摘The future Sixth-Generation (6G) wireless systems are expected to encounter emerging services with diverserequirements. In this paper, 6G network resource orchestration is optimized to support customized networkslicing of services, and place network functions generated by heterogeneous devices into available resources.This is a combinatorial optimization problem that is solved by developing a Particle Swarm Optimization (PSO)based scheduling strategy with enhanced inertia weight, particle variation, and nonlinear learning factor, therebybalancing the local and global solutions and improving the convergence speed to globally near-optimal solutions.Simulations show that the method improves the convergence speed and the utilization of network resourcescompared with other variants of PSO.
基金the National Key Research and Development Program of China(No.2022ZD0119003)the National Natural Science Foundation of China(No.61834005).
文摘Hyperparameter optimization is considered as one of the most challenges in deep learning and dominates the precision of model in a certain.Recent proposals tried to solve this issue through the particle swarm optimization(PSO),but its native defect may result in the local optima trapped and convergence difficulty.In this paper,the genetic operations are introduced to the PSO,which makes the best hyperparameter combination scheme for specific network architecture be located easier.Spe-cifically,to prevent the troubles caused by the different data types and value scopes,a mixed coding method is used to ensure the effectiveness of particles.Moreover,the crossover and mutation opera-tions are added to the process of particles updating,to increase the diversity of particles and avoid local optima in searching.Verified with three benchmark datasets,MNIST,Fashion-MNIST,and CIFAR10,it is demonstrated that the proposed scheme can achieve accuracies of 99.58%,93.39%,and 78.96%,respectively,improving the accuracy by about 0.1%,0.5%,and 2%,respectively,compared with that of the PSO.
基金The work is funded by the University Grant Commission(UGC)under(Start-up-Grant No.:F 30-592/2021(BSR)).
文摘Feature selection(FS)is a data preprocessing step in machine learning(ML)that selects a subset of relevant and informative features from a large feature pool.FS helps ML models improve their predictive accuracy at lower computational costs.Moreover,FS can handle the model overfitting problem on a high-dimensional dataset.A major problem with the filter and wrapper FS methods is that they consume a significant amount of time during FS on high-dimensional datasets.The proposed“HDFS(PSO-MI):hybrid distribute feature selection using particle swarm optimization-mutual information(PSO-MI)”,is a PSO-based hybrid method that can overcome the problem mentioned above.This method hybridizes the filter and wrapper techniques in a distributed manner.A new combiner is also introduced to merge the effective features selected from multiple data distributions.The effectiveness of the proposed HDFS(PSO-MI)method is evaluated using five ML classifiers,i.e.,logistic regression(LR),k-NN,support vector machine(SVM),decision tree(DT),and random forest(RF),on various datasets in terms of accuracy and Matthew’s correlation coefficient(MCC).From the experimental analysis,we observed that HDFS(PSO-MI)method yielded more than 98%,95%,92%,90%,and 85%accuracy for the unbalanced,kidney disease,emotions,wafer manufacturing,and breast cancer datasets,respectively.Our method shows promising results comapred to other methods,such as mutual information,gain ratio,Spearman correlation,analysis of variance(ANOVA),Pearson correlation,and an ensemble feature selection with ranking method(EFSRank).
文摘Updating the velocity in particle swarm optimization (PSO) consists of three terms: the inertia term, the cognitive term and the social term. The balance of these terms determines the balance of the global and local search abilities, and therefore the performance of PSO. In this work, an adaptive parallel PSO algorithm, which is based on the dynamic exchange of control parameters between adjacent swarms, has been developed. The proposed PSO algorithm enables us to adaptively optimize inertia factors, learning factors and swarm activity. By performing simulations of a search for the global minimum of a benchmark multimodal function, we have found that the proposed PSO successfully provides appropriate control parameter values, and thus good global optimization performance.
基金the National Natural Science Foundation of China (Nos. 60625302 and 60704028)the Program for ChangjiangScholars and Innovative Research Team in University (No. IRT0721)+2 种基金the 111 Project (No. B08021)the Major State Basic Research De-velopment Program of Shanghai (No. 07JC14016)ShanghaiLeading Academic Discipline Project (No. B504) of China
文摘Particle swarm optimization (PSO) is an efficient, robust and simple optimization algorithm. Most studies are mainly concentrated on better understanding of the standard PSO control parameters, such as acceleration coefficients, etc. In this paper, a more simple strategy of PSO algorithm called θ-PSO is proposed. In θ-PSO, an increment of phase angle vector replaces the increment of velocity vector and the positions are decided by the mapping of phase angles. Benchmark testing of nonlinear func- tions is described and the results show that the performance of θ-PSO is much more effective than that of the standard PSO.
文摘Particle swarm optimization （PSO）, like other evolutionary algorithms is a population-based stochastic algorithm inspired from the metaphor of social interaction in birds, insects, wasps, etc. It has been used for finding promising solutions in complex search space through the interaction of particles in a swarm. It is a well recognized fact that the performance of evolutionary algorithms to a great extent depends on the choice of appropriate strategy/operating parameters like population size, crossover rate, mutation rate, crossover operator, etc. Generally, these parameters are selected through hit and trial process, which is very unsystematic and requires rigorous experimentation. This paper proposes a systematic based on Taguchi method reasoning scheme for rapidly identifying the strategy parameters for the PSO algorithm. The Taguchi method is a robust design approach using fractional factorial design to study a large number of parameters with small number of experiments. Computer simulations have been performed on two benchmark functionsiRosenbrock function and Griewank functionito validate the approach.
基金The National Key Technologies R & D Program during the 11th Five-Year Plan Period(No.2009BAG13A04)the Ph.D.Programs Foundation of Ministry of Education of China(No.200802861061)the Transportation Science Research Project of Jiangsu Province(No.08X09)
文摘In order to improve the accuracy and reliability of the driving fatigue detection based on a single feature, a new detection algorithm based on multiple features is proposed. Two direct driver＇s facial features reflecting fatigue and one indirect vehicle behavior feature indicating fatigue are considered. Meanwhile, T-S fuzzy neural network（TSFNN）is adopted to recognize the driving fatigue of drivers. For the structure identification of the TSFNN, subtractive clustering（SC） is used to confirm the fuzzy rules and their correlative parameters. Moreover, the particle swarm optimization （PSO）algorithm is improved to train the TSFNN. Simulation results and experiments on vehicles show that the proposed algorithm can effectively improve the convergence speed and the recognition accuracy of the TSFNN, as well as enhance the correct rate of driving fatigue detection.
文摘In shock wave＇s pressure testing,a dynamic compensation digital filter is designed based on particle swarm optimization （PSO） algorithm.Dynamic calibration experiment and simulation are conducted for the pressure sensor.PSO algorithm is applied on Matlab platform to achieve optimization according to input and output data of the sensor as well as the reference model,and the global optimal values got by optimization become the parameters of the compensator.Finally,the dynamic compensation filter is established on LabVIEW platform.The experimental results show that the data after processing with the compensation filter truly reflects the input signal.
文摘The common failure mechanism for brittle rocks is known to be axial splitting which happens parallel to the direction of maximum compression. One of the mechanisms proposed for modelling of axial splitting is the sliding crack or so called, “wing crack” model. Fairhurst-Cook model explains this specific type of failure which starts by a pre-crack and finally breaks the rock by propagating 2-D cracks under uniaxial compression. In this paper, optimization of this model has been considered and the process has been done by a complete sensitivity analysis on the main parameters of the model and excluding the trends of their changes and also their limits and “peak points”. Later on this paper, three artificial intelligence algorithms including Particle Swarm Intelligence (PSO), Ant Colony Optimization (ACO) and genetic algorithm (GA) has been used and compared in order to achieve optimized sets of parameters resulting in near-maximum or near-minimum amounts of wedging forces creating a wing crack.
基金supported in part by the National Natural Science Foundation of China (62372385, 62272078, 62002337)the Chongqing Natural Science Foundation (CSTB2022NSCQ-MSX1486, CSTB2023NSCQ-LZX0069)the Deanship of Scientific Research at King Abdulaziz University, Jeddah, Saudi Arabia (RG-12-135-43)。
文摘High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation learning to an HDI matrix,whose hyper-parameter adaptation can be implemented through a particle swarm optimizer(PSO) to meet scalable requirements.However, conventional PSO is limited by its premature issues,which leads to the accuracy loss of a resultant LFA model. To address this thorny issue, this study merges the information of each particle's state migration into its evolution process following the principle of a generalized momentum method for improving its search ability, thereby building a state-migration particle swarm optimizer(SPSO), whose theoretical convergence is rigorously proved in this study. It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss. Experiments on six HDI matrices indicate that an SPSO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational efficiency.Hence, SPSO's use ensures efficient and reliable hyper-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices.
基金supported by the National Natural Sciences Foundation of China(61603069,61533005,61522304,U1560102)the National Key Research and Development Program of China(2017YFA0700300)
文摘For multi-objective optimization problems, particle swarm optimization(PSO) algorithm generally needs a large number of fitness evaluations to obtain the Pareto optimal solutions. However, it will become substantially time-consuming when handling computationally expensive fitness functions. In order to save the computational cost, a surrogate-assisted PSO with Pareto active learning is proposed. In real physical space(the objective functions are computationally expensive), PSO is used as an optimizer, and its optimization results are used to construct the surrogate models. In virtual space, objective functions are replaced by the cheaper surrogate models, PSO is viewed as a sampler to produce the candidate solutions. To enhance the quality of candidate solutions, a hybrid mutation sampling method based on the simulated evolution is proposed, which combines the advantage of fast convergence of PSO and implements mutation to increase diversity. Furthermore, ε-Pareto active learning(ε-PAL)method is employed to pre-select candidate solutions to guide PSO in the real physical space. However, little work has considered the method of determining parameter ε. Therefore, a greedy search method is presented to determine the value ofεwhere the number of active sampling is employed as the evaluation criteria of classification cost. Experimental studies involving application on a number of benchmark test problems and parameter determination for multi-input multi-output least squares support vector machines(MLSSVM) are given, in which the results demonstrate promising performance of the proposed algorithm compared with other representative multi-objective particle swarm optimization(MOPSO) algorithms.
基金supported by the Specialized Research Fund for the Doctoral Program of Higher Education(20114307120032)the National Natural Science Foundation of China(71201167)
文摘As for the drop of particle diversity and the slow convergent speed of particle in the late evolution period when particle swarm optimization（PSO） is applied to solve high-dimensional multi-modal functions,a hybrid optimization algorithm based on the cat mapping,the cloud model and PSO is proposed.While the PSO algorithm evolves a certain of generations,this algorithm applies the cat mapping to implement global disturbance of the poorer individuals,and employs the cloud model to execute local search of the better individuals;accordingly,the obtained best individuals form a new swarm.For this new swarm,the evolution operation is maintained with the PSO algorithm,using the parameter of pop distr to balance the global and local search capacity of the algorithm,as well as,adopting the parameter of mix gen to control mixing times of the algorithm.The comparative analysis is carried out on the basis of 4 functions and other algorithms.It indicates that this algorithm shows faster convergent speed and better solving precision for solving functions particularly those high-dimensional multi-modal functions.Finally,the suggested values are proposed for parameters pop distr and mix gen applied to different dimension functions via the comparative analysis of parameters.
基金supported by National Natural Science Foundation of China(61425008,61333004,61273054)Top-Notch Young Talents Program of China,and Aeronautical Foundation of China(2013585104)
基金supported by the Council of Scientific and Industrial Research of India(09/028(0947)/2015-EMR-I)
文摘This paper proposes an artificial neural network(ANN) based software reliability model trained by novel particle swarm optimization(PSO) algorithm for enhanced forecasting of the reliability of software. The proposed ANN is developed considering the fault generation phenomenon during software testing with the fault complexity of different levels. We demonstrate the proposed model considering three types of faults residing in the software. We propose a neighborhood based fuzzy PSO algorithm for competent learning of the proposed ANN using software failure data. Fitting and prediction performances of the neighborhood fuzzy PSO based proposed neural network model are compared with the standard PSO based proposed neural network model and existing ANN based software reliability models in the literature through three real software failure data sets. We also compare the performance of the proposed PSO algorithm with the standard PSO algorithm through learning of the proposed ANN. Statistical analysis shows that the neighborhood fuzzy PSO based proposed neural network model has comparatively better fitting and predictive ability than the standard PSO based proposed neural network model and other ANN based software reliability models. Faster release of software is achievable by applying the proposed PSO based neural network model during the testing period.
基金supported by the National Natural Science Foundation of China (60873086)the Aeronautical Science Foundation of China(20085153013)the Fundamental Research Found of Northwestern Polytechnical Unirersity (JC200942)
文摘An improved particle swarm optimization（PSO） algorithm is proposed to train the fuzzy support vector machine（FSVM） for pattern multi-classification.In the improved algorithm,the particles studies not only from itself and the best one but also from the mean value of some other particles.In addition,adaptive mutation was introduced to reduce the rate of premature convergence.The experimental results on the synthetic aperture radar（SAR） target recognition of moving and stationary target acquisition and recognition（MSTAR） dataset and character recognition of MNIST database show that the improved algorithm is feasible and effective for fuzzy multi-class SVM training.
基金supported by the National Natural Science Foundation of China (61873079,51707050)
文摘Interval type-2 fuzzy neural networks(IT2FNNs)can be seen as the hybridization of interval type-2 fuzzy systems(IT2FSs) and neural networks(NNs). Thus, they naturally inherit the merits of both IT2 FSs and NNs. Although IT2 FNNs have more advantages in processing uncertain, incomplete, or imprecise information compared to their type-1 counterparts, a large number of parameters need to be tuned in the IT2 FNNs,which increases the difficulties of their design. In this paper,big bang-big crunch(BBBC) optimization and particle swarm optimization(PSO) are applied in the parameter optimization for Takagi-Sugeno-Kang(TSK) type IT2 FNNs. The employment of the BBBC and PSO strategies can eliminate the need of backpropagation computation. The computing problem is converted to a simple feed-forward IT2 FNNs learning. The adoption of the BBBC or the PSO will not only simplify the design of the IT2 FNNs, but will also increase identification accuracy when compared with present methods. The proposed optimization based strategies are tested with three types of interval type-2 fuzzy membership functions(IT2FMFs) and deployed on three typical identification models. Simulation results certify the effectiveness of the proposed parameter optimization methods for the IT2 FNNs.
