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Hybrid Hierarchical Particle Swarm Optimization with Evolutionary Artificial Bee Colony Algorithm for Task Scheduling in Cloud Computing
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作者 Shasha Zhao Huanwen Yan +3 位作者 Qifeng Lin Xiangnan Feng He Chen Dengyin Zhang 《Computers, Materials & Continua》 SCIE EI 2024年第1期1135-1156,共22页
Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment.Short execution time and low load imbalance may be the chall... Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment.Short execution time and low load imbalance may be the challenges for some algorithms in resource scheduling scenarios.In this work,the Hierarchical Particle Swarm Optimization-Evolutionary Artificial Bee Colony Algorithm(HPSO-EABC)has been proposed,which hybrids our presented Evolutionary Artificial Bee Colony(EABC),and Hierarchical Particle Swarm Optimization(HPSO)algorithm.The HPSO-EABC algorithm incorporates both the advantages of the HPSO and the EABC algorithm.Comprehensive testing including evaluations of algorithm convergence speed,resource execution time,load balancing,and operational costs has been done.The results indicate that the EABC algorithm exhibits greater parallelism compared to the Artificial Bee Colony algorithm.Compared with the Particle Swarm Optimization algorithm,the HPSO algorithmnot only improves the global search capability but also effectively mitigates getting stuck in local optima.As a result,the hybrid HPSO-EABC algorithm demonstrates significant improvements in terms of stability and convergence speed.Moreover,it exhibits enhanced resource scheduling performance in both homogeneous and heterogeneous environments,effectively reducing execution time and cost,which also is verified by the ablation experimental. 展开更多
关键词 Cloud computing distributed processing evolutionary artificial bee colony algorithm hierarchical particle swarm optimization load balancing
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Multi-Topology Hierarchical Collaborative Hybrid Particle Swarm Optimization Algorithm for WSN 被引量:1
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作者 Yi Wang Kanqi Wang +2 位作者 Maosheng Zhang Hongzhi Zheng Hui Zhang 《China Communications》 SCIE CSCD 2023年第8期254-275,共22页
Wireless sensor networks(WSN)are widely used in many situations,but the disordered and random deployment mode will waste a lot of sensor resources.This paper proposes a multi-topology hierarchical collaborative partic... Wireless sensor networks(WSN)are widely used in many situations,but the disordered and random deployment mode will waste a lot of sensor resources.This paper proposes a multi-topology hierarchical collaborative particle swarm optimization(MHCHPSO)to optimize sensor deployment location and improve the coverage of WSN.MHCHPSO divides the population into three types topology:diversity topology for global exploration,fast convergence topology for local development,and collaboration topology for exploration and development.All topologies are optimized in parallel to overcome the precocious convergence of PSO.This paper compares with various heuristic algorithms at CEC 2013,CEC 2015,and CEC 2017.The experimental results show that MHCHPSO outperforms the comparison algorithms.In addition,MHCHPSO is applied to the WSN localization optimization,and the experimental results confirm the optimization ability of MHCHPSO in practical engineering problems. 展开更多
关键词 particle swarm optimizer levy flight multi-topology hierarchical collaborative framework lamarckian learning intuitive fuzzy entropy wireless sensor network
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A Chaotic Local Search-Based Particle Swarm Optimizer for Large-Scale Complex Wind Farm Layout Optimization 被引量:3
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作者 Zhenyu Lei Shangce Gao +2 位作者 Zhiming Zhang Haichuan Yang Haotian Li 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2023年第5期1168-1180,共13页
Wind energy has been widely applied in power generation to alleviate climate problems.The wind turbine layout of a wind farm is a primary factor of impacting power conversion efficiency due to the wake effect that red... Wind energy has been widely applied in power generation to alleviate climate problems.The wind turbine layout of a wind farm is a primary factor of impacting power conversion efficiency due to the wake effect that reduces the power outputs of wind turbines located in downstream.Wind farm layout optimization(WFLO)aims to reduce the wake effect for maximizing the power outputs of the wind farm.Nevertheless,the wake effect among wind turbines increases significantly as the number of wind turbines increases in the wind farm,which severely affect power conversion efficiency.Conventional heuristic algorithms suffer from issues of low solution quality and local optimum for large-scale WFLO under complex wind scenarios.Thus,a chaotic local search-based genetic learning particle swarm optimizer(CGPSO)is proposed to optimize large-scale WFLO problems.CGPSO is tested on four larger-scale wind farms under four complex wind scenarios and compares with eight state-of-the-art algorithms.The experiment results indicate that CGPSO significantly outperforms its competitors in terms of performance,stability,and robustness.To be specific,a success and failure memories-based selection is proposed to choose a chaotic map for chaotic search local.It improves the solution quality.The parameter and search pattern of chaotic local search are also analyzed for WFLO problems. 展开更多
关键词 Chaotic local search(CLS) evolutionary computation genetic learning particle swarm optimization(pso) wake effect wind farm layout optimization(WFLO)
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Quantitative algorithm for airborne gamma spectrum of large sample based on improved shuffled frog leaping-particle swarm optimization convolutional neural network 被引量:1
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作者 Fei Li Xiao-Fei Huang +5 位作者 Yue-Lu Chen Bing-Hai Li Tang Wang Feng Cheng Guo-Qiang Zeng Mu-Hao Zhang 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2023年第7期242-252,共11页
In airborne gamma ray spectrum processing,different analysis methods,technical requirements,analysis models,and calculation methods need to be established.To meet the engineering practice requirements of airborne gamm... In airborne gamma ray spectrum processing,different analysis methods,technical requirements,analysis models,and calculation methods need to be established.To meet the engineering practice requirements of airborne gamma-ray measurements and improve computational efficiency,an improved shuffled frog leaping algorithm-particle swarm optimization convolutional neural network(SFLA-PSO CNN)for large-sample quantitative analysis of airborne gamma-ray spectra is proposed herein.This method was used to train the weight of the neural network,optimize the structure of the network,delete redundant connections,and enable the neural network to acquire the capability of quantitative spectrum processing.In full-spectrum data processing,this method can perform the functions of energy spectrum peak searching and peak area calculations.After network training,the mean SNR and RMSE of the spectral lines were 31.27 and 2.75,respectively,satisfying the demand for noise reduction.To test the processing ability of the algorithm in large samples of airborne gamma spectra,this study considered the measured data from the Saihangaobi survey area as an example to conduct data spectral analysis.The results show that calculation of the single-peak area takes only 0.13~0.15 ms,and the average relative errors of the peak area in the U,Th,and K spectra are 3.11,9.50,and 6.18%,indicating the high processing efficiency and accuracy of this algorithm.The performance of the model can be further improved by optimizing related parameters,but it can already meet the requirements of practical engineering measurement.This study provides a new idea for the full-spectrum processing of airborne gamma rays. 展开更多
关键词 Large sample Airborne gamma spectrum(AGS) Shuffled frog leaping algorithm(SFLA) particle swarm optimization(pso) Convolutional neural network(CNN)
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Neural network hyperparameter optimization based on improved particle swarm optimization
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作者 谢晓燕 HE Wanqi +1 位作者 ZHU Yun YU Jinhao 《High Technology Letters》 EI CAS 2023年第4期427-433,共7页
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
关键词 hyperparameter optimization particle swarm optimization(pso)algorithm neu-ral network
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θ-PSO: a new strategy of particle swarm optimization 被引量:7
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作者 Wei-min ZHONG Shao-jun LI Feng QIAN 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2008年第6期786-790,共5页
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
关键词 particle swarm optimization (pso Phase angle Benchmark function
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Driving fatigue fusion detection based on T-S fuzzy neural network evolved by subtractive clustering and particle swarm optimization 被引量:6
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作者 孙伟 张为公 +1 位作者 李旭 陈刚 《Journal of Southeast University(English Edition)》 EI CAS 2009年第3期356-361,共6页
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
关键词 driving fatigue fusion detection particle swarm optimization(pso) subtractive clustering(SC)
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Dynamic compensation for sensors based on particle swarm optimization and realization on LabVIEW 被引量:1
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作者 张霞 张志杰 陈保立 《Journal of Measurement Science and Instrumentation》 CAS 2014年第1期25-28,共4页
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
关键词 particle swarm optimization (pso dynamic compensation LABVIEW
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Optimization of Fairhurst-Cook Model for 2-D Wing Cracks Using Ant Colony Optimization (ACO), Particle Swarm Intelligence (PSO), and Genetic Algorithm (GA)
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作者 Mohammad Najjarpour Hossein Jalalifar 《Journal of Applied Mathematics and Physics》 2018年第8期1581-1595,共15页
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
关键词 WING Crack Fairhorst-Cook Model Sensitivity Analysis optimization particle swarm INTELLIGENCE (pso) Ant Colony optimization (ACO) Genetic Algorithm (GA)
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Hybrid optimization algorithm based on chaos,cloud and particle swarm optimization algorithm 被引量:29
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作者 Mingwei Li Haigui Kang +1 位作者 Pengfei Zhou Weichiang Hong 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2013年第2期324-334,共11页
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
关键词 particle swarm optimization(pso) chaos theory cloud model hybrid optimization
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A Predator-prey Particle Swarm Optimization Approach to Multiple UCAV Air Combat Modeled by Dynamic Game Theory 被引量:27
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作者 Haibin Duan Pei Li Yaxiang Yu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI 2015年第1期11-18,共8页
Dynamic game theory has received considerable attention as a promising technique for formulating control actions for agents in an extended complex enterprise that involves an adversary. At each decision making step, e... Dynamic game theory has received considerable attention as a promising technique for formulating control actions for agents in an extended complex enterprise that involves an adversary. At each decision making step, each side seeks the best scheme with the purpose of maximizing its own objective function. In this paper, a game theoretic approach based on predatorprey particle swarm optimization (PP-PSO) is presented, and the dynamic task assignment problem for multiple unmanned combat aerial vehicles (UCAVs) in military operation is decomposed and modeled as a two-player game at each decision stage. The optimal assignment scheme of each stage is regarded as a mixed Nash equilibrium, which can be solved by using the PP-PSO. The effectiveness of our proposed methodology is verified by a typical example of an air military operation that involves two opposing forces: the attacking force Red and the defense force Blue. © 2014 Chinese Association of Automation. 展开更多
关键词 Aircraft control AIRSHIPS Combinatorial optimization Computation theory Decision making Military operations Military vehicles particle swarm optimization (pso)
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Surrogate-Assisted Particle Swarm Optimization Algorithm With Pareto Active Learning for Expensive Multi-Objective Optimization 被引量:13
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作者 Zhiming Lv Linqing Wang +2 位作者 Zhongyang Han Jun Zhao Wei Wang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2019年第3期838-849,共12页
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
关键词 MULTIOBJECTIVE optimization PARETO active learning particle swarm optimization (pso) surrogate
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Forecasting of Software Reliability Using Neighborhood Fuzzy Particle Swarm Optimization Based Novel Neural Network 被引量:11
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作者 Pratik Roy Ghanshaym Singha Mahapatra Kashi Nath Dey 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2019年第6期1365-1383,共19页
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
关键词 Artificial neural network(ANN) FUZZY particle swarm optimization(pso) RELIABILITY prediction software RELIABILITY
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Improved particle swarm optimization algorithm for fuzzy multi-class SVM 被引量:17
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作者 Ying Li Bendu Bai Yanning Zhang 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2010年第3期509-513,共5页
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
关键词 particle swarm optimization(pso) fuzzy support vector machine(FSVM) adaptive mutation multi-classification.
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Particle swarm optimization-based algorithm of a symplectic method for robotic dynamics and control 被引量:5
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作者 Zhaoyue XU Lin DU +1 位作者 Haopeng WANG Zichen DENG 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI CSCD 2019年第1期111-126,共16页
Multibody system dynamics provides a strong tool for the estimation of dynamic performances and the optimization of multisystem robot design. It can be described with differential algebraic equations(DAEs). In this pa... Multibody system dynamics provides a strong tool for the estimation of dynamic performances and the optimization of multisystem robot design. It can be described with differential algebraic equations(DAEs). In this paper, a particle swarm optimization(PSO) method is introduced to solve and control a symplectic multibody system for the first time. It is first combined with the symplectic method to solve problems in uncontrolled and controlled robotic arm systems. It is shown that the results conserve the energy and keep the constraints of the chaotic motion, which demonstrates the efficiency, accuracy, and time-saving ability of the method. To make the system move along the pre-planned path, which is a functional extremum problem, a double-PSO-based instantaneous optimal control is introduced. Examples are performed to test the effectiveness of the double-PSO-based instantaneous optimal control. The results show that the method has high accuracy, a fast convergence speed, and a wide range of applications.All the above verify the immense potential applications of the PSO method in multibody system dynamics. 展开更多
关键词 ROBOTIC DYNAMICS MULTIBODY system SYMPLECTIC method particle swarm optimization(pso)algorithm instantaneous optimal control
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Sensors deployment optimization in multi-dimensional space based on improved particle swarm optimization algorithm 被引量:9
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作者 TANG Mingnan CHEN Shijun +2 位作者 ZHENG Xuehe WANG Tianshu CAO Hui 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2018年第5期969-982,共14页
Sensors deployment optimization has become one of the most attractive fields in recent years. However, most of the previous work focused on the deployment problem in 2D space.Compared to the traditional form, sensors ... Sensors deployment optimization has become one of the most attractive fields in recent years. However, most of the previous work focused on the deployment problem in 2D space.Compared to the traditional form, sensors deployment in multidimensional space has greater research significance and practical potential to satisfy the detecting needs in complex environment.Aiming at solving this issue, a multi-dimensional space sensor network model is established, and the radar system is selected as an example. Considering the possible working mode of the radar system(e.g., searching and tracking), two distinctive deployment models are proposed based on maximum coverage area and maximum target detection probability in the attack direction respectively. The latter one is usually ignored in the previous literature.For uncovering the optimal deployment of the sensor network, the particle swarm optimization(PSO) algorithm is improved using the proposed weights determination scheme, in which the linear decreasing, the pooling strategy and the cloud theory are combined for weights updating. Experimental results illustrate the effectiveness of the proposed method. 展开更多
关键词 spatial sensor optimized deployment strategy particle swarm optimization(pso)
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Improved Oustaloup approximation of fractional-order operators using adaptive chaotic particle swarm optimization 被引量:6
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作者 Zhe Gao Xiaozhong Liao 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2012年第1期145-153,共9页
A rational approximation method of the fractional-order derivative and integral operators is proposed. The turning fre- quency points are fixed in each frequency interval in the standard Oustaloup approximation. In th... A rational approximation method of the fractional-order derivative and integral operators is proposed. The turning fre- quency points are fixed in each frequency interval in the standard Oustaloup approximation. In the improved Oustaloup method, the turning frequency points are determined by the adaptive chaotic particle swarm optimization (PSO). The average velocity is proposed to reduce the iterations of the PSO. The chaotic search scheme is combined to reduce the opportunity of the premature phenomenon. Two fitness functions are given to minimize the zero-pole and amplitude-phase frequency errors for the underlying optimization problems. Some numerical examples are compared to demonstrate the effectiveness and accuracy of this proposed rational approximation method. 展开更多
关键词 fractional-order calculus rational approximation particle swarm optimization (pso tent map.
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Optimal choice of parameters for particle swarm optimization 被引量:14
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作者 张丽平 俞欢军 胡上序 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2005年第6期528-534,共7页
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
关键词 particle swarm optimization (pso) Constriction factor method (CFM) Parameter selection
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Genetic algorithm and particle swarm optimization tuned fuzzy PID controller on direct torque control of dual star induction motor 被引量:13
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作者 BOUKHALFA Ghoulemallah BELKACEM Sebti +1 位作者 CHIKHI Abdesselem BENAGGOUNE Said 《Journal of Central South University》 SCIE EI CAS CSCD 2019年第7期1886-1896,共11页
This study presents analysis, control and comparison of three hybrid approaches for the direct torque control (DTC) of the dual star induction motor (DSIM) drive. Its objective consists of combining three different he... This study presents analysis, control and comparison of three hybrid approaches for the direct torque control (DTC) of the dual star induction motor (DSIM) drive. Its objective consists of combining three different heuristic optimization techniques including PID-PSO, Fuzzy-PSO and GA-PSO to improve the DSIM speed controlled loop behavior. The GA and PSO algorithms are developed and implemented into MATLAB. As a result, fuzzy-PSO is the most appropriate scheme. The main performance of fuzzy-PSO is reducing high torque ripples, improving rise time and avoiding disturbances that affect the drive performance. 展开更多
关键词 dual star induction motor drive direct torque control particle swarm optimization (pso) fuzzy logic control genetic algorithms
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Support vector machine forecasting method improved by chaotic particle swarm optimization and its application 被引量:11
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作者 李彦斌 张宁 李存斌 《Journal of Central South University》 SCIE EI CAS 2009年第3期478-481,共4页
By adopting the chaotic searching to improve the global searching performance of the particle swarm optimization (PSO), and using the improved PSO to optimize the key parameters of the support vector machine (SVM) for... By adopting the chaotic searching to improve the global searching performance of the particle swarm optimization (PSO), and using the improved PSO to optimize the key parameters of the support vector machine (SVM) forecasting model, an improved SVM model named CPSO-SVM model was proposed. The new model was applied to predicting the short term load, and the improved effect of the new model was proved. The simulation results of the South China Power Market’s actual data show that the new method can effectively improve the forecast accuracy by 2.23% and 3.87%, respectively, compared with the PSO-SVM and SVM methods. Compared with that of the PSO-SVM and SVM methods, the time cost of the new model is only increased by 3.15 and 4.61 s, respectively, which indicates that the CPSO-SVM model gains significant improved effects. 展开更多
关键词 chaotic searching particle swarm optimization (pso support vector machine (SVM) short term load forecast
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