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作者： Zifa Liu Dong Peng Xinyue Wang Hui Li Hongyang Liu North China Electric Power University State Grid Economic and Technological Research Institute CO.LTD

For the multi-criteria decision-making problem of portfolio investment in power grid construction projects,the modern portfolio theory is used to construct a portfolio optimization model based on adaptive particle swarm optimization algorithm under the constraints of power demand,grid enterprise investment capability and reliability level in this *** the shortcomings of slow convergence and long operation time of particle swarm optimization,the change of inertia weight is used to change the update mode of particle position,which improves the convergence speed of the *** portfolio optimization model provides an applicable decision-making method for complex grid optimization investment decisions,and builds a complex grid optimization investment decision management process based on power demand and investment capability.

关键词： optimization model power grid project investment particle swarm optimization algorithm

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Lifetime Enhancement of Wireless Sensor Networks by using Sine Cosine optimization algorithm 7

Lifetime Enhancement of Wireless Sensor Networks by using Si...

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7th International Conference on Reliability, Infocom Technologies and optimization - Trends and Future Directions (ICRITO)

作者： Pandey, Ashish Rajan, Abhishek Nandi, Arnab Natl Inst Technol Silchar Dept Elect & Commun Engn Cachar 788010 Assam India Natl Inst Technol Sikkim Dept Elect & Elect Engn Sikkim 737139 India

ISBN: (纸本)9781538646922

Wireless sensor networks (WSNs) have numerous applications from the measurements of atmospheric quantity, tracking to the various medical applications. Sensor nodes in the networks are mostly battery powered and they are having limited lifetime as it is not always feasible to replace their battery. To overcome this constraint of energy various energy management scheme have been proposed and implemented. In this paper we have used Sine Cosine algorithm (SCA) in routing and clustering for enhancing the working lifetime of WSNs. As the cluster head (CH) in the networks having more energy expenditure so we have also used some higher energy nodes to perform CHs operations for the enhancement of the lifetime. We have compared the results with the other algorithms namely particle swarm optimization (PSO) algorithm, genetic algorithm (GA) and least distance clustering (LDC) algorithm. Results are also have been studied after moving the base station at different locations.

关键词： WSNs Sine Cosine optimization algorithm Genetic algorithm particle swarm optimization algorithm PSO Clustering Routing

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Performance Evaluation of optimization algorithms based on GPU using CUDA Architecture

Performance Evaluation of Optimization Algorithms based on G...

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5th IEEE Latin American Conference on Computational Intelligence (LA-CCI)

作者： Kawano, Yunkio Valdez, Fevrier Castillo, Oscar Tijuana Inst Technol Tijuana Baja California Mexico

ISBN: (纸本)9781538646267

In this paper, an evaluation was made on the performance of two of the most commonly used optimization algorithms, which are the particle swarm optimization (PSO) algorithm and the Genetic algorithm (GA). In this case, average time of execution is compared, executing the algorithm using the original code on the traditional processor against the modified algorithm where certain processes of the algorithm are integrated into the video card. These changes demonstrate a significant improvement in execution time. On the other hand a graphical interface was made for each one of the optimization algorithms to facilitate the process of handling the parameters.

关键词： particle swarm optimization algorithm Genetic algorithm CUDA Architecture

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Feature Selection for Cancer Classification Based on SRPSO algorithm

Feature Selection for Cancer Classification Based on SRPSO A...

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2019 International Conference on Informatics, Control and Robotics 2019信息学、控制和机器人学国际会议(ICICR2019)

作者： Qiu-lan XIAO Hong ZHENG Qing-an YAO School of Computer Science and Engineering Changchun University of Technology

Improving the accuracy of cancer classification plays an important role in cancer-assisted diagnosis. Genes selection is an important factor for improving the accuracy of cancer classification. In this paper, based on the standard particle swarm optimization algorithm, an SRPSO algorithm with self-adaptive and reverse-learning mechanism is proposed. It is applied to select feature genes from microarray datasets, and the results are used for cancer classification via SVM to make 5-fold cross-validation. To evaluate the performance of SRPSO, four different cancer datasets including Colon, ALLML, MLL, and SRBCT were selected. Based on the evaluation process, the SRPSO algorithm provided better results on each dataset.

关键词： Microarray gene data Cancer classification particle swarm optimization algorithm Self-adaptive Reverse-learning

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Improved Power Sharing with a Back-to-Back Converter and State-Feedback Control in a Utility-Connected Microgrid 2

Improved Power Sharing with a Back-to-Back Converter and Sta...

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2nd IEEE Texas Power and Energy Conference (TPEC)

作者： Jafari, Hassan Mahmoudi, Maryam Fatehi, Alireza Naderi, Mohammad H. Kaya, Elif Amirkabir Univ Technol Elect Engn Dept Tehran Iran KN Toosi Univ Technol Elect Engn Dept Tehran Iran Texas A&M Univ Elect Engn Dept College Stn TX USA

ISBN: (纸本)9781538610060

This study puts forward a strategy for optimal power sharing control in a microgrid that is connected to a utility grid through a back-to-back (B2B) converter. In grid-connected mode, the B2B converter totally isolates the microgrid from the utility grid in terms of voltage and frequency. In the proposed strategy, a pre-specified amount of power exchanged between the utility grid and the microgrid is regulated via active/reactive control in the B2B converter. This regulation means that distributed generation (DG) units supply the rest of microgrid load demand and track load changes through droop control. In both the voltage source inverter (VSI) of the B2B converter and the DG units, state-feedback control is employed as an inner control loop for tracking the state variable reference signals generated by the corresponding outer loops of the variables. Microgrid stability is essential and highly affected by controller parameters, droop coefficients, and the components of inductor-capacitor-inductor filters. In this regard, control is formulated as an optimization problem, for which particle swarm optimization is used to optimally calculate the parameters of the system and the controllers. Objective functions are derived by minimizing an eigenvalue-based function. The PSCAD simulation results demonstrate the effectiveness of the proposed control strategy.

关键词： Back-to-back converter particle swarm optimization algorithm grid-connected mode angle droop power sharing state-feedback control

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Research on the Influence of Aerobic Exercise on Athletes' Body Shape and Function Based on Multiple Linear Regression

Research on the Influence of Aerobic Exercise on Athletes' B...

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作者： Xiaojing Zhou Shanghai Lixin Accounting Institute of Finance

Cardiopulmonary Function Test of Athletes is the Key to Scientifically and Reasonably Formulate Training Plans. in Order to Solve the Problem of Large Errors in the Existing Cardiorespiratory Function Detection Methods, a Multiple Linear Regression Cardiorespiratory Function Detection Method Based on particle swarm optimization is Proposed. through Significant Difference Correlation Evaluation, the Metabolic Circulation Function in Sports is Analyzed to Realize Comprehensive Evaluation of Athletes' Absolute Strength, Speed Strength and Strength Endurance, and the Internal Relationship between Athletes' Aerobic Metabolism Ability and Anaerobic Metabolism Ability is Obtained. the Results Show That 3 Months Aerobic Exercise Can Obviously Improve the Body Shape and Physiological Function of Young Women. particle swarm optimization is Used to Optimize and Improve the Speed and Accuracy of Cardiopulmonary Function Detection. the Method Can Effectively Improve the Cardiopulmonary Function of Athletes Before and after Aerobic Training, and Has High Modeling Accuracy.

关键词： Multiple Linear Regression particle swarm optimization algorithm Aerobic Exercise Body Shape Function

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Prediction Model of MBR Membrane Flux for Elman Neural Network Based on PSO-GA Hybrid algorithm 8

Prediction Model of MBR Membrane Flux for Elman Neural Netwo...

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8th International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC)

作者： Wang, Xinchang Li, Chunqing Tianjin Polytech Univ Coll Comp Sci & Software Tianjin Peoples R China

ISBN: (数字)9781538682463

ISBN: (纸本)9781538682463

Membrane fouling reduces wastewater treatment efficiency and cause financial and energy costs to some extent. The size of membrane flux reflects the degree of membrane pollution. Make timely cleaning membrane or replacement membrane decision to maintain considerable treatment effect on the basis of the membrane flux. particle swarm optimization (PSO) algorithm can quickly find the global optimum. Genetic algorithm (GA) has the property of global convergence. The prediction model used in this paper is based on the PSO-GA hybrid algorithm. The combination of these not only improves the convergence speed of genetic algorithm but also reduces the probability of particle swarm optimization algorithm falling into local optimum. Elman neural network acts as the basic network. Compared with Elman neural network and BP neural network, the prediction accuracy of PSO-GA-Elman is improved.

关键词： MBR particle swarm optimization algorithm Genetic algorithm Prediction Model

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Development of particle swarm optimization algorithm

Development of Particle Swarm Optimization Algorithm

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The 6th International Conference on Computer Science & Education(第六届国际计算机新科技与教育学术会议 ICCSE 2011)

作者： Yu Chen Fan Yang Quan Zou Chen Lin School of Information Science & Technology Xiamen UniversityChina

particle swarm optimization (PSO) is a new stochastic optimization technique based on swarm intelligence. In this paper, we introduce the basic principles of PSO firstly. Then, the research progress on PSO algorithm is summarized in several fields, such as parameter selection and design, population topology, hybrid PSO algorithm etc. Finally, some vital applications and aspects that may be conducted in the future investigations are discussed.

关键词： component swarm intelligence evolutionary computation particle swarm optimization algorithm

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An Intelligent Parameter Selection Method for particle swarm optimization algorithm

An Intelligent Parameter Selection Method for Particle Swarm...

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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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