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应用完备集合固有时间尺度分解和混合差分进化和粒子群算法优化的最小二乘支持向量机对柴油机进行故障诊断 Article

俊红 张,昱 刘

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 2,   Pages 272-286 doi: 10.1631/FITEE.1500337

Abstract: 针对固有时间尺度分解算法的模态混叠问题和最小二乘支持向量机的参数优化问题，本文提出了一种新的基于完备集合固有时间尺度分解和混合差分进化和粒子群算法优化最小二乘支持向量机的柴油机故障诊断方法。该方法主要包括以下几个步骤：首先，为解决固有时间尺度分解算法的模态混叠问题，提出了一种完备集合固有时间尺度分解算法。随后，利用完备集合固有时间尺度分解算法将非平稳的柴油机振动信号分解为一系列平稳的旋转分量和残差信号。最后，提出了混合差分进化和粒子群算法对最小二乘支持向量机的参数进行优化的方法，并通过将故障特征输入训练好的最小二乘支持向量机模型实现故障诊断。仿真和实验结果表明提出的故障诊断方法可以克服固有时间尺度分解的模态混叠问题，而且能够准确的识别柴油机故障。

Keywords： 柴油机；故障诊断；完备集合固有时间尺度分解；最小二乘支持向量机；混合差分进化和粒子群优化算法    

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利用对称结构和结合差分进化的文化算法检测阵列中的故障传感器 Article

Shafqat Ullah KHAN,Ijaz Mansoor QURESHI,Fawad ZAMAN,Wasim KHAN

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 2,   Pages 235-245 doi: 10.1631/FITEE.1500315

Abstract: 本文首先提出了一种线性阵列的对称结构，其次，基于结合差分进化的文化算法，建立了一种混合技术。通过Monte Carlo模拟对该方案性能进行了验证，并在计算时间和均方误差方面与现有方法进行了比较。

Keywords： 文化算法；差分进化；线性对称传感器阵列    

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Solving Knapsack Problem by Hybrid Particle Swarm Optimization Algorithm

Gao Shang,Yang Jingyu

Strategic Study of CAE 2006, Volume 8, Issue 11,   Pages 94-98

Abstract:

The classical particle swarm optimization is a powerful method to find the minimum of a numerical function, on a continuous definition domain. The particle swarm optimization algorithm combining with the idea of the genetic algorithm is recommended to solve knapsack problem. All the 6 hybrid particle swarm optimization algorithms are proved effective. Especially the hybrid particle swarm optimization algorithm derived from across strategy A and mutation strategy C is a simple yet effective algorithm and it has been applied successfully to investment problem. It can easily be modified for any combinatorial problem for which there has been no good specialized algorithm.

Keywords： particle swarm algorithm     knapsack problem     genetic algorithm     mutation    

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A hybrid-model optimization algorithm based on the Gaussian process and particle swarm optimization for mixed-variable CNN hyperparameter automatic search Research Article

Han YAN, Chongquan ZHONG, Yuhu WU, Liyong ZHANG, Wei LU

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 11,   Pages 1557-1573 doi: 10.1631/FITEE.2200515

Abstract: s (CNNs) have been developed quickly in many real-world fields. However, CNN’s performance depends heavily on its hyperparameters, while finding suitable hyperparameters for CNNs working in application fields is challenging for three reasons: (1) the problem of encoding for different types of hyperparameters in CNNs, (2) expensive computational costs in evaluating candidate hyperparameter configuration, and (3) the problem of ensuring convergence rates and model performance during hyperparameter search. To overcome these problems and challenges, a hybrid-model optimization algorithm is proposed in this paper to search suitable hyperparameter configurations automatically based on the and (GPPSO) algorithm. First, a new encoding method is designed to efficiently deal with the CNN hyperparameter problem. Second, a hybrid-surrogate-assisted model is proposed to reduce the high cost of evaluating candidate hyperparameter configurations. Third, a novel activation function is suggested to improve the model performance and ensure the convergence rate. Intensive experiments are performed on imageclassification benchmark datasets to demonstrate the superior performance of GPPSO over state-of-the-art methods. Moreover, a case study on metal fracture diagnosis is carried out to evaluate the GPPSO algorithm performance in practical applications. Experimental results demonstrate the effectiveness and efficiency of GPPSO, achieving accuracy of 95.26% and 76.36% only through 0.04 and 1.70 GPU days on the CIFAR-10 and CIFAR-100 datasets, respectively.

Keywords： Convolutional neural network     Gaussian process     Hybrid model     Hyperparameter optimization     Mixed-variable     Particle swarm optimization    

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Survey on Particle Swarm Optimization Algorithm

Yang Wei,Li Chiqiang

Strategic Study of CAE 2004, Volume 6, Issue 5,   Pages 87-94

Abstract:

Particle swarm optimization (PSO) is a new optimization technique originating from artificial life and evolutionary computation. The algorithm completes the optimization through following the personal best solution of each particle and the global best value of the whole swarm. PSO can be implemented with ease and few parameters need to be tuned. It has been successfully applied in many areas. In this paper, the basic principles of PSO are introduced at length, and various improvements and applications of PSO are also presented. Finally, some future research directions about PSO are proposed.

Keywords： swarm intelligence     evolutionary algorithm     particle swarm optimization    

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Application prospect of PSO in hydrology

Dong Qianjin,Cao Guangjing,Wang Xianjia,Dai Huichao,Zhao Yunfa

Strategic Study of CAE 2010, Volume 12, Issue 1,   Pages 81-85

Abstract:

The basic algorithm and its flow are introduced at first, then its application to scheduling operation of reservoir, economic operation of hydropower and parameter calibration in hydrology field is discussed, the suggestion for future study is pointed out that should strengthen the study of adaptive mechanism and convergence performance in PSO, compare and combine with other technology, broaden the region of application to hydrology which may supply a new method for solving much optimal problem in hydrology field.

Keywords： hydrology science     particle swarm optimization     scheduling operation     economical operation    

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ECGID: a human identification method based on adaptive particle swarm optimization and the bidirectional LSTM model Research Article

Yefei Zhang, Zhidong Zhao, Yanjun Deng, Xiaohong Zhang, Yu Zhang,zhangyf@hdu.edu.cn,zhaozd@hdu.edu.cn,yanjund@hdu.edu.cn,xhzhang@hdu.edu.cn,zy2009@hdu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 12,   Pages 1551-1684 doi: 10.1631/FITEE.2000511

Abstract: Physiological signal based biometric analysis has recently attracted attention as a means of meeting increasing privacy and security requirements. The real-time nature of an electrocardiogram (ECG) and the hidden nature of the information make it highly resistant to attacks. This paper focuses on three major bottlenecks of existing deep learning driven approaches: the lengthy time requirements for optimizing the hyperparameters, the slow and computationally intense identification process, and the unstable and complicated nature of ECG acquisition. We present a novel deep neural network framework for learning feature representations directly from ECG time series. The proposed framework integrates deep bidirectional long short-term memory (BLSTM) and . The overall approach not only avoids the inefficient and experience-dependent search for hyperparameters, but also fully exploits the spatial information of ordinal local features and the memory characteristics of a recognition algorithm. The effectiveness of the proposed approach is thoroughly evaluated in two ECG datasets, using two protocols, simulating the influence of electrode placement and acquisition sessions in identification. Comparing four recurrent neural network structures and four classical machine learning and deep learning algorithms, we prove the superiority of the proposed algorithm in minimizing overfitting and self-learning of time series. The experimental results demonstrated an average identification rate of 97.71%, 99.41%, and 98.89% in training, validation, and test sets, respectively. Thus, this study proves that the application of APSO and LSTM techniques to biometric can achieve a lower algorithm engineering effort and higher capacity for generalization.

Keywords： 心电图生物特征；个体身份识别；长短期记忆网络；自适应粒子群优化算法    

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Multi-objective particle swarm cooperative optimization algorithm for state parameters

Ding Lei,Wu Min,She Jinhua,Duan Ping

Strategic Study of CAE 2010, Volume 12, Issue 2,   Pages 101-107

Abstract:

To deal with the characters with the strong nonlinear and complex computing of synthetic permeability and burn-through point in the lead-zinc sintering process, an efficient multi-objective particle swarm cooperative optimization algorithm is proposed. Firstly, the multi-objective optimization model for burn-through point and synthetic permeability is established. Secondly, an improved multi-objective particle swarm cooperative optimization algorithm is presented by improving the constraint comparison method and the way of selecting the particles' optima, and using different swarms to optimize corresponding variables respectively. Finally, the proposed multi-objective optimization algorithm is applied to optimize the synthetic permeability and the burn-through point. The simulation results show that the proposed multi-objective optimization algorithm effectively solves the optimization problem of the synthetic permeability and burn-through point.

Keywords： lead-zinc sintering process     synthetic permeability     burn-through point     multi-objective particle swarm cooperative optimization algorithm    

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Differential evolution based computation intelligence solver for elliptic partial differential equations Research Article

Muhammad Faisal Fateh, Aneela Zameer, Sikander M. Mirza, Nasir M. Mirza, Muhammad Saeed Aslam, Muhammad Asif Zahoor Raja,muhammad.aslam@adelaide.edu.au

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 10,   Pages 1445-1456 doi: 10.1631/FITEE.1900221

Abstract: A based methodology is introduced for the solution of elliptic s (PDEs) with Dirichlet and/or Neumann boundary conditions. The solutions evolve over bounded domains throughout the interior nodes by minimization of nodal deviations among the population. The elliptic PDEs are replaced by the corresponding system of finite difference approximation, yielding an expression for nodal residues. The global residue is declared as the root-mean-square value of the nodal residues and taken as the cost function. The standard is then used for the solution of elliptic PDEs by conversion to a minimization problem of the global residue. A set of benchmark problems consisting of both linear and nonlinear elliptic PDEs has been considered for validation, proving the effectiveness of the proposed algorithm. To demonstrate its robustness, sensitivity analysis has been carried out for various operators and parameters. Comparison of the based computed nodal values with the corresponding data obtained using the exact analytical expressions shows the accuracy and convergence of the proposed methodology.

Keywords： 差分进化；边界值问题；偏微分方程；有限差分法；数值计算    

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A scheduling method based on a hybrid genetic particle swarm algorithm for multifunction phased array radar Article

Hao-wei ZHANG, Jun-wei XIE, Wen-long LU, Chuan SHENG, Bin-feng ZONG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 11,   Pages 1806-1816 doi: 10.1631/FITEE.1601358

Abstract: A hybrid optimization approach combining a particle swarm algorithm, a genetic algorithm, and a heuristic inter-leaving algorithm is proposed for scheduling tasks in the multifunction phased array radar. By optimizing parameters using chaos theory, designing the dynamic inertia weight for the particle swarm algorithm as well as introducing crossover operation and mutation operation of the genetic algorithm, both the efficiency and exploration ability of the hybrid algorithm are improved. Under the frame of the intelligence algorithm, the heuristic interleaving scheduling algorithm is presented to further use the time resource of the task waiting duration. A large-scale simulation demonstrates that the proposed algorithm is more robust and effi-cient than existing algorithms.

Keywords： Phased array radar     Scheduling     Particle swarm algorithm     Genetic algorithm     Pulse interleave    

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Flight control for air-breathing hypersonic vehicles using linear quadratic regulator design based on stochastic robustness analysis Article

Lin CAO, Shuo TANG, Dong ZHANG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 7,   Pages 882-897 doi: 10.1631/FITEE.1601363

Abstract: The flight dynamics model of air-breathing hypersonic vehicles (AHVs) is highly nonlinear and multivariable cou-pling, and includes inertial uncertainties and external disturbances that require strong, robust, and high-accuracy controllers. In this paper, we propose a linear-quadratic regulator (LQR) design method based on stochastic robustness analysis for the longitudinal dynamics of AHVs. First, input/output feedback linearization is used to design LQRs. Second, subject to various system parameter uncertainties, system robustness is characterized by the probability of stability and desired performance. Then, the mapping rela-tionship between system robustness and LQR parameters is established. Particularly, to maximize system robustness, a novel hybrid particle swarm optimization algorithm is proposed to search for the optimal LQR parameters. During the search iteration, a Chernoff bound algorithm is applied to determine the finite sample size of Monte Carlo evaluation with the given probability levels. Finally, simulation results show that the optimization algorithm can effectively find the optimal solution to the LQR parameters.

Keywords： Air-breathing hypersonic vehicles (AHVs)     Stochastic robustness analysis     Linear-quadratic regulator (LQR)     Particle swarm optimization (PSO)     Improved hybrid PSO algorithm    

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Prediction of vibration response of powerhouse structures based on LS-SVM optimized by PSO

Lian Jijian,He Longjun,Wang Haijun

Strategic Study of CAE 2011, Volume 13, Issue 12,   Pages 45-50

Abstract:

The vibration of powerhouse structures is mainly induced by hydraulics factors, mechanical and electromagnetic factors of the generating unit. It nonlinearly couples with the generating unit. Based on prototype observation data of Ertan Hydropower Station, the paper analyzes the coupling effect between vibration of units and powerhouse,and then the vibration response forecasting model of the powerhouse is built based on LS-SVM optimized by particle swarm optimization algorithm, and the prediction results are coincide with the observed data. Further, the paper introduces the running water head as an input divisor into the intelligent prediction model while the forecasting range is extended, and the result is satisfactory.

Keywords： powerhouse     coupled vibration     particle swarm optimization algorithm     least squares support vector machines     response prediction    

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Using improved particle swarm optimization totune PID controllers in cooperative collision avoidance systems Article

Xing-chen WU, Gui-he QIN, Ming-hui SUN, He YU, Qian-yi XU

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 9,   Pages 1385-1395 doi: 10.1631/FITEE.1601427

Abstract: The introduction of proportional-integral-derivative (PID) controllersinto cooperative collision avoidance systems (CCASs) has been hinderedby difficulties in their optimization and by a lack of study of theireffects on vehicle driving stability, comfort, and fuel economy. Inthis paper, we propose a method to optimize PID controllers usingan improved particle swarm optimization (PSO) algorithm, and to bettermanipulate cooperative collision avoidance with other vehicles. First,we use PRESCAN and MATLAB/Simulink to conduct a united simulation,which constructs a CCAS composed of a PID controller, maneuver strategyjudging modules, and a path planning module. Then we apply the improvedPSO algorithm to optimize the PID controller based on the dynamicvehicle data obtained. Finally, we perform a simulation test of performancebefore and after the optimization of the PID controller, in whichvehicles equipped with a CCAS undertake deceleration driving and steeringunder the two states of low speed (≤50 km/h) and high speed (≥100km/h) cruising. The results show that the PID controller optimizedusing the proposed method can achieve not only the basic functionsof a CCAS, but also improvements in vehicle dynamic stability, ridingcomfort, and fuel economy.

Keywords： Cooperative collision avoidance system (CCAS)     Improved particle swarm optimization (PSO)     PID controller     Vehicle comfort     Fuel economy    

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Using PSO to update pheromone for the traveling salesman problem

Cheng Weiming,Tang Zhenmin,Zhao Chunxia,Chen Debao

Strategic Study of CAE 2008, Volume 10, Issue 7,   Pages 165-168

Abstract:

Using pheromone of ant coloney system as reference, a novel method of solving TSP problem is proposed. That is using particle swarm optimization ( PSO) . PSO is used because of its simple operation, easy implementation and faster speed. In order to improve the popularity of the particle swarm,make the particle swam not to homogeneous too fast and decrease the possibility of local constrain, the algorithm decides the number of degenerated particles based on a designated popularity function.Experiment results and comparison studies have demonstrated that our work is useful.

Keywords： pheromone     particle swarm algorithm     TSP    

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Research on economic operation of microgrid with high temperature energy storage system

Luo Yi and Zhang Lijuan

Strategic Study of CAE 2015, Volume 17, Issue 1,   Pages 74-80

Abstract:

With the advantages of high efficiency， environmental protection and energy conservation， the high energy storage system has extensive application prospect， economic operation is becoming a wide concerned issue on microgrid with high temperature energy storage system. By analyzing the microgrid with high temperature energy storage system， based on the characteristics of each micro-source， the model of high temperature energy storage system and economic operation model of the grid-connected microgrid are constructed with time-sharing electricity. The improved immune particle swarm algorithm is used to solve the proposed model， and then field application verifies the effectiveness of model. Results show that the proposed method and model can reach the globally optimal solution of dynamic microgrid， and it is of cost saving and significant economic benefit for high temperature energy storage system to participate in thermal load supplying.

Keywords： high temperature energy storage system； microgrid； economic operation； optimized dispatching； improved immune particle swarm algorithm    

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Title Author Date Type Operation

应用完备集合固有时间尺度分解和混合差分进化和粒子群算法优化的最小二乘支持向量机对柴油机进行故障诊断

俊红 张,昱 刘

Journal Article

利用对称结构和结合差分进化的文化算法检测阵列中的故障传感器

Shafqat Ullah KHAN,Ijaz Mansoor QURESHI,Fawad ZAMAN,Wasim KHAN

Journal Article

Solving Knapsack Problem by Hybrid Particle Swarm Optimization Algorithm

Gao Shang,Yang Jingyu

Journal Article

A hybrid-model optimization algorithm based on the Gaussian process and particle swarm optimization for mixed-variable CNN hyperparameter automatic search

Han YAN, Chongquan ZHONG, Yuhu WU, Liyong ZHANG, Wei LU

Journal Article

Survey on Particle Swarm Optimization Algorithm

Yang Wei,Li Chiqiang

Journal Article

Application prospect of PSO in hydrology

Dong Qianjin,Cao Guangjing,Wang Xianjia,Dai Huichao,Zhao Yunfa

Journal Article

ECGID: a human identification method based on adaptive particle swarm optimization and the bidirectional LSTM model

Yefei Zhang, Zhidong Zhao, Yanjun Deng, Xiaohong Zhang, Yu Zhang,zhangyf@hdu.edu.cn,zhaozd@hdu.edu.cn,yanjund@hdu.edu.cn,xhzhang@hdu.edu.cn,zy2009@hdu.edu.cn

Journal Article

Multi-objective particle swarm cooperative optimization algorithm for state parameters

Ding Lei,Wu Min,She Jinhua,Duan Ping

Journal Article

Differential evolution based computation intelligence solver for elliptic partial differential equations

Muhammad Faisal Fateh, Aneela Zameer, Sikander M. Mirza, Nasir M. Mirza, Muhammad Saeed Aslam, Muhammad Asif Zahoor Raja,muhammad.aslam@adelaide.edu.au

Journal Article

A scheduling method based on a hybrid genetic particle swarm algorithm for multifunction phased array radar

Hao-wei ZHANG, Jun-wei XIE, Wen-long LU, Chuan SHENG, Bin-feng ZONG

Journal Article

Flight control for air-breathing hypersonic vehicles using linear quadratic regulator design based on stochastic robustness analysis

Lin CAO, Shuo TANG, Dong ZHANG

Journal Article

Prediction of vibration response of powerhouse structures based on LS-SVM optimized by PSO

Lian Jijian,He Longjun,Wang Haijun

Journal Article

Using improved particle swarm optimization totune PID controllers in cooperative collision avoidance systems

Xing-chen WU, Gui-he QIN, Ming-hui SUN, He YU, Qian-yi XU

Journal Article

Using PSO to update pheromone for the traveling salesman problem

Cheng Weiming,Tang Zhenmin,Zhao Chunxia,Chen Debao

Journal Article

Research on economic operation of microgrid with high temperature energy storage system

Luo Yi and Zhang Lijuan

Journal Article