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An Intelligent Parameter Selection Method for particle swarm optimization algorithm

An Intelligent Parameter Selection Method for Particle Swarm...

The Fourth International Joint Conference on Computational Science and optimization(第四届计算科学与优化国际大会 CSO 2011)

作者： Yuntao Dai Ying Li Liqiang Liu College of Science Harbin Engineering University Heilongjiang China College of Automation Harbin Engineering University Heilongjiang China

For the problem of particle swarm optimization parameters selection, a kind of intelligent method to optimum parameters selection using another particle swarm optimization algorithm is proposed. Firstly it analyzes the effect of each parameter on algorithm performance in detail. Then it takes parameter selection of PSO algorithm as a complex optimization problem, sets appropriate fitness function to describe optimization performance, and uses PSO-PARA algorithm to optimize the parameters selection method of PSO-OPT algorithm. Tests to the benchmark function show that these parameters are better than the experience parameters test results in the optimal fitness, the mean value of optimal fitness, convergence rate.

关键词： parameter optimization particle swarm optimization algorithm analysis of parameters

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A Study on Fault Diagnosis of Rotating Machinery Combined Wavelet Transform with VMD

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Journal of Physics: Conference Series 2020年第1期1626卷

作者： Huan Zhou Hao Wang Machinery and Energy Engineering College Shanghai University of Electric Power Shanghai 200090

Effective diagnosis of rotating machinery is difficult in view of the complex structure, weak early fault signals, non-stationary and non-linear vibration signals, and low signal-to-noise ratio. In this paper, a fault diagnosis method is proposed based on particle swarm optimization (PSO) and variational modal decomposition (VMD). Firstly, wavelet packet threshold is denoised on the signal, VMD is decomposed on the reconstructed signal, and PSO is optimized on the inherent mode function (IMF) obtained from decomposition so as to determine the best IMF function. Then Hilbert transform and envelope spectrum analysis are carried out on the IMF function, and the envelope spectrum analysis result is compared with theoretical calculation frequency to finally determine the fault type. The results indicate that this method can effectively reduce noise components in signals, extract weak fault information and realize fault diagnosis.

关键词： Wavelet packet denoising VMD IMF particle swarm optimization algorithm Rotating machinery

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Developing an optimized artificial intelligence model for S&P 500 option pricing: A hybrid GARCH model

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INTERNATIONAL JOURNAL OF FINANCIAL ENGINEERING 2020年第3期7卷

作者： Hajizadeh, Ehsan Amirkabir Univ Technol Tehran Polytech Dept Ind Engn & Management Syst 424 Hafez Ave Tehran *** Iran

In this paper, we propose two hybrid models to release some limitations and enhancement of the results. In this regard, three popular GARCH-type models are utilized for more accurate estimating of volatility, as the most important parameter for option pricing. Furthermore, the two non-parametric models based on Artificial Neural Networks and Neuro-Fuzzy Networks tuned by particle swarm optimization algorithm are proposed to price call options for the S&P 500 index. By comparing the results obtained using these models, we conclude that both Neural Network and Neuro-Fuzzy Network models outperform the Black-Scholes model.

关键词： Option pricing volatility GARCH-type models neural network neuro-fuzzy network particle swarm optimization algorithm

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The State of Charge Estimation of Power Lithium Battery Based on RBF Neural Network Optimized by particle swarm optimization

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淡江理工学刊 2017年第4期20卷 483-490页

作者： Zhixiang Hou Ping Xie Jiqiang Hou

In order to ensure the safe and stable operation of electric vehicles (EV), it is necessary to accurately estimate the state of charge (SOC) of power lithium battery for electric vehicle. Because of the nonlinear relationship between SOC and its influencing factors, RBF neural network has obvious advantages in solving nonlinear problems, so in this paper, an SOC estimation method of power battery based on RBF neural network is proposed. In order to improve the accuracy of SOC estimation, we use particle swarm optimization (PSO) to optimize the RBF neural network model and identify the value of RBF network center vector and the weights through global optimal searching ability of PSO algorithm. The results simulation show that the SOC model based on PSO-RBF neural network has good estimation accuracy.

关键词： Electric Vehicle State-of-charge Estimation Radial Basis Function Neural Network particle swarm optimization algorithm

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Ascent Trajectory optimization of Hypersonic Vehicle Based on Improved particle swarm algorithm

Ascent Trajectory Optimization of Hypersonic Vehicle Based o...

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Chinese Automation Congress (CAC)

作者： Wu, Ge Liu, Lei Wang, Yongji Liu, Xing Huazhong Univ Sci & Technol Sch Automat Natl Key Lab Sci & Technol Multispectral Informat Wuhan 430074 Peoples R China

ISBN: (纸本)9781467371896

By introducing the adaptive inertia weight, the time factor and the structure rebuilding of particle swarm optimization (PSO), the improvements of PSO are completed. In order to improve the accuracy and convergence speed, a PSO strategy is proposed, which consists of the dynamic population structure, opposition-based learning, crossover operator and variable step integral. Combining the improvement of PSO and the optimization strategy, the modified particle swarm optimization ( MPSO) algorithm is formed. The MPSO is applied to optimize the ascent trajectory of hypersonic vehicle. The precision and efficiency of this trajectory optimization method are demonstrated by comparing the results of PSO and MPSO. The simulation results show that the performance of MPSO is significantly superior to PSO either convergence speed or convergent accuracy.

关键词： trajectory optimization particle swarm optimization algorithm variable population opposition-based learning crossover operator

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Integration of particle swarm optimization and Immune Genetic algorithm-Based Dynamic Clustering for Customer Clustering

Integration of Particle Swarm Optimization and Immune Geneti...

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8th Panhellenic Conference on Artificial Intelligence

作者： Kuo, R. J. Lin, S. H. Chen, Zhen-Yao Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei 10607 Taiwan Taiwan Semicond Mfg Co Hsinchu 30078 Taiwan De Lin Inst Technol Dept Business Adm New Taipei City 23654 Taiwan

This study intends to present a dynamic clustering (DC) approach based on particle swarm optimization (PSO) and immune genetic (IG) (DCPIG) algorithm, which is able to cluster the data into adequate clusters through data characteristics with pre-specified numbers of clusters. The proposed DCPIG algorithm is compared with three DC algorithms in the literature using Iris, Wine, Glass and Vowel benchmark data sets. The experiment results show that the DCPIG algorithm can achieve higher stability and accuracy than the other algorithms. In addition, the DCPIG algorithm is also applied to a real-world problem considering the customer clustering for a cyber flower shop. Lastly, we recommend different products and services to customers based on the clustering results.

关键词： Cluster analysis dynamic clustering particle swarm optimization algorithm immune genetic algorithm

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Optimal Design for a Retrial Queueing System with State-Dependent Service Rate

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Journal of Systems Science & Complexity 2017年第4期30卷 883-900页

作者： ZHANG Xuelu WANG Jinting MA Qing Department of Mathematics Beijing Jiaotong University School of Science Qilu University of Technology Department of Basic Courses Shandong Women’s University

This paper considers a single server retrial queue in which a state-dependent service policy is adopted to control the service rate. Customers arrive in the system according to a Poisson process and the service times and inter-retrial times are all exponentially distributed. If the number of customers in orbit is equal to or less than a certain threshold, the service rate is set in a low value and it also can be switched to a high value once this number exceeds the threshold. The stationary distribution and two performance measures are obtained through the partial generating functions. It is shown that this state-dependent service policy degenerates into a classic retrial queueing system without control policy under some conditions. In order to achieve the social optimal strategies, a new reward-cost function is established and the global numerical solutions, obtained by Canonical particle swarm optimization algorithm, demonstrate that the managers can get more benefits if applying this state-dependent service policy compared with the classic model.

关键词： particle swarm optimization algorithm retrial queues social optimal strategy statedependent service policy

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Integrated parallel forecasting model based on modified fuzzy time series and SVM

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Journal of Systems Engineering and Electronics 2017年第4期28卷 766-775页

作者： Yong Shuai Tailiang Song Jianping Wang Technical Support Engineering Faculty Academy of Armored Forced Engineering Beijing 100072 China Unit 68207 of the PLA Jiayuguan 735100 China China Defense Science & Technology Information Center Beijing 100142 China

A dynamic parallel forecasting model is proposed, which is based on the problem of current forecasting models and their combined model. According to the process of the model, the fuzzy C-means clustering algorithm is improved in outliers operation and distance in the clusters and among the clusters. Firstly, the input data sets are optimized and their coherence is ensured, the region scale algorithm is modified and non-isometric multi scale region fuzzy time series model is built. At the same time, the particle swarm optimization algorithm about the particle speed, location and inertia weight value is improved, this method is used to optimize the parameters of support vector machine, construct the combined forecast model, build the dynamic parallel forecast model, and calculate the dynamic weight values and regard the product of the weight value and forecast value to be the final forecast values. At last, the example shows the improved forecast model is effective and accurate.

关键词： fuzzy C-means clustering fuzzy time series interval partitioning support vector machine particle swarm optimization algorithm parallel forecasting

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Fast basis search for adaptive Fourier decomposition

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2018年第1期2018卷 1页

作者： Wang, Ze Wan, Feng Wong, Chi Man Qian, Tao Univ Macau Dept Elect & Comp Engn Fac Sci & Technol Ave Univ Taipa Macau Peoples R China Univ Macau Dept Math Fac Sci & Technol Ave Univ Taipa Macau Peoples R China

The adaptive Fourier decomposition (AFD) uses an adaptive basis instead of a fixed basis in the rational analytic function and thus achieves a fast energy convergence rate. At each decomposition level, an important step is to determine a new basis element from a dictionary to maximize the extracted energy. The existing basis searching method, however, is only the exhaustive searching method that is rather inefficient. This paper proposes four methods to accelerate the AFD algorithm based on four typical optimization techniques including the unscented Kalman filter (UKF) method, the Nelder-Mead (NM) algorithm, the genetic algorithm (GA), and the particle swarm optimization (PSO) algorithm. In the simulation of decomposing four representative signals and real ECG signals, compared with the existing exhaustive search method, the proposed schemes can achieve much higher computation speed with a fast energy convergence, that is, in particular, to make the AFD possible for real-time applications.

关键词： Adaptive Fourier decomposition Unscented Kalman filter Nelder-Mead algorithm Genetic algorithm particle swarm optimization algorithm

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Prediction of Novel High-Pressure Structures of Magnesium Niobium Dihydride

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ACS APPLIED MATERIALS & INTERFACES 2017年第31期9卷 26169-26176页

作者： Zhang, Chuanzhao Sun, Guoliang Wang, Jingjing Lu, Cheng Jin, Yuanyuan Kuang, Xiaoyu Hermann, Andreas Sichuan Univ Inst Atom & Mol Phys Chengdu 610065 Sichuan Peoples R China Nanyang Normal Univ Dept Phys Nanyang 473061 Peoples R China Yangtze Univ Dept Phys & Optoelect Engn Jingzhou 434023 Peoples R China Univ Nevada Dept Phys Las Vegas NV 89154 USA Univ Nevada High Pressure Sci & Engn Ctr Las Vegas NV 89154 USA Univ Edinburgh Sch Phys & Astron Ctr Sci Extreme Condit Edinburgh EH9 3JZ Midlothian Scotland Univ Edinburgh Sch Phys & Astron SUPA Edinburgh EH9 3JZ Midlothian Scotland

On the basis of a combination of the particle-swarm optimization technique and density functional theory (DFT), we explore the crystal structures of MgH2, NbH2, and MgNbH2 under high pressure. The enthalpy-pressure (H-P) diagrams indicate that the structural transition sequence of MgH2 isay alpha -> gamma -> delta -> epsilon -> zeta and that NbH2 transforms from the Fm (3) over barm phase to the Pnma phase at 47.80 GPa. However, MgNbH2 is unstable when the pressure is too low or too high. Two novel MgNbH2 structures, the hexagonal P (6) over bar m2 phase and the orthorhombic Cmcm phase, are discovered, which are stable in the pressure ranges of 13.24-128.27 GPa and 128.27-186.77 GPa, respectively. The P (6) over bar m2 phase of MgNbH2 consists of alternate layers of polymetric NbH6 and MgH6 triangular prisms, while the Cmcm phase contains distorted MgH6 trigonal prisms. The calculated elastic constants and phonon dispersions confirm that both phases are mechanically and dynamically stable. The analyses of density of states (DOS), electron localization function (ELF), and Bader charge demonstrate that a combination of ionic and metallic bonds exist in both P (6) over bar m2 and Cmcm phases. We hope the newly predicted magnesium niobium dihydrides with desirable electronic properties will promote future experimental and theoretical studies on mixed main group-transition metal hydrides.

关键词： magnesium niobium dihydride particle swarm optimization algorithm density functional theory structural transition electronic structures

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