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Parametric analysis and optimization of entropy generation in unsteady MHD flow over a stretching rotating disk using artificial neural network and particle swarm optimization algorithm

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ENERGY 2013年 55卷 497-510页

作者： Rashidi, M. M. Ali, M. Freidoonimehr, N. Nazari, F. Bu Ali Sina Univ Fac Engn Dept Mech Engn Hamadan Iran King Saud Univ Coll Engn Dept Mech Engn Riyadh 11421 Saudi Arabia Islamic Azad Univ Hamedan Branch Young Researchers & Elites Club Hamadan Iran

The present study first of all concerns the first and second law analyzes of an electrically conducting fluid past a rotating disk in the presence of a uniform vertical magnetic field, analytically via Homotopy Analysis Method (HAM), and then applies Artificial Neural Network (ANN) and particle swarm optimization (PSO) algorithm in order to minimize the entropy generation. In the first part of this study, entropy generation equation is derived as a function of velocity and temperature gradients and non-dimensionalized using geometrical and physical flow field-dependent parameters. A very good agreement can be seen between some of the obtained results of the current study and the results of the previously published data. The effects of physical flow parameters such as magnetic interaction parameter, unsteadiness parameter, disk stretching parameter, Prandtl number, Reynolds number and Brinkman number on all fluid velocity components, temperature distribution and the averaged entropy generation number are checked and analyzed. For minimizing the entropy generation value a procedure based on ANN and PSO is proposed. This procedure comprises three steps. The first step is to find entropy generation for values of some different affecting factors. In the second step, some distinct multi-layer perceptron ANNs based on the data obtained from step one are trained. In step three, PSO is used to minimize the entropy generation in the considered stretchable rotating disk. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： MHD flow Entropy generation Unsteady flow Rotating disk particle swarm optimization algorithm Artificial neural network

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Parameters optimization of hybrid strategy recommendation based on particle swarm algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 168卷 114388-114388页

作者： Cai, Biao Zhu, Xinping Qin, Yangxin Chengdu Univ Technol Coll Informat Sci & Technol Chengdu 610059 Peoples R China

With the unprecedented development in the internet technology, the information overload issues have become more and more complex, resulting in users being unable to obtain the target information accurately and effectively in selecting the required information from a large pool of surfed data. In view of this a recommendation system can be used to predict the user's selection probability for different potential objects as an important tool, which can help to solve the information overload issues. So far, many personalized recommendation algorithms based on bipartite graphS have been proposed, most of which are based on the similarity degree among users or items, such as collaborative filtering (CF), mass diffusion (MD) and heat conduction (HC). Among many recommendation algorithms, the performances of algorithms are varied. MD algorithm has high recommendation accuracy but poor diversity, while HC algorithm has good diversity but low accuracy. In order to solve the dilemma in accuracy and diversity, some hybrid recommendation algorithm have been proposed. This paper has mainly focused on the hybrid recommendation algorithm HHM, and pointed out its shortcomings. Based on the reconsideration of the effect of item popularity in the recommendation process, an improved hybrid recommendation algorithm using dual parameter called IHM was proposed. The particle swarm optimization (PSO) algorithm was applied to the parameter optimal of the hybrid recommendation algorithm to obtain the parameters of the algorithm. Experiments on 3 real datasets indicated that the IHM algorithm is better than HHM algorithms in terms of the recommendation accuracy, diversity and novelty. Meanwhile, the IHM algorithm can also improve the recommendation for items with lower popularity and solve the cold start problem.

关键词： Recommendation system Personalized recommendation Information overload Hybrid recommendation algorithm particle swarm optimization algorithm

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Underwater remotely operated vehicle control system with optimized PID based on improved particle swarm optimization

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DESALINATION AND WATER TREATMENT 2023年 314卷 322-329页

作者： Liu, Weixing Xia, Zihan Wu, Linyan Guo, Guangkuo Zhu, Cheng Zhang, Zhiyang Cui, Lin Jiangsu Ocean Univ Sc Ocean Engn Lianyungang 222005 Peoples R China Jiangsu Ocean Univ Makarov Coll Marine Engn Lianyungang 222005 Peoples R China Minist Ind & Informat Technol Res Inst Elect 5 Guangzhou 510000 Peoples R China Harbin Engn Univ Sci & Technol Underwater Vehicle Lab Harbin 150001 Peoples R China Nat Ocean Technol Ctr Tianjin 300112 Peoples R China

On account of the exponential development of control theory and artificial intelligence technology in recent years, more and more control theory is applied to the control of robots, but PID control method due to the simple structure and good stability still has research value, the core and the difficulty is the optimization of PID parameters. In order to solve the underwater remotely operated vehicle (ROV) with high nonlinearity and strong coupling characteristics, it can adjust its attitude in time to ensure its control performance in the face of the disturbance of the external complex environment. By improving the particle swarm inertia weights, the particle swarm optimization (PSO) algorithm reduces the situation of falling into the local optimal solution and applies it to the PID adaptive parameterization. Comparing the improved PSO-PID with the traditional PID simulation, it is concluded that the improved PSO-PID control has certain improvement on the control performance of the ROV, which has certain feasibility.

关键词： Remotely operated vehicle PID control particle swarm optimization algorithm

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Automatic boiling water reactor control rod pattern design using particle swarm optimization algorithm and local search

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NUCLEAR ENGINEERING AND DESIGN 2013年 255卷 273-279页

作者： Wang, Cheng-Der Lin, Chaung Inst Nucl Energy Res Nucl Engn Div Longtan Township 32546 Taoyuan County Taiwan Natl Tsing Hua Univ Dept Engn & Syst Sci Hsinchu 30013 Taiwan

This study developed a method for the automatic design of a boiling water reactor (BWR) control rod pattern (CRP) using the particle swarm optimization (PSO) algorithm. The PSO algorithm is more random compared to the rank-based ant system (RAS) that was used to solve the same BWR CRP design problem in the previous work. In addition, the local search procedure was used to make improvements after PSO, by adding the single control rod (CR) effect. The design goal was to obtain the CRP so that the thermal limits and shutdown margin would satisfy the design requirement and the cycle length, which is implicitly controlled by the axial power distribution, would be acceptable. The results showed that the same acceptable CRP found in the previous work could be obtained. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Boiling water reactor control rod pattern particle swarm optimization algorithm Local search

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Path Planning of Continuum Robot Based on a New Improved particle swarm optimization algorithm

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Journal of Harbin Institute of Technology(New Series) 2013年第4期20卷 78-84页

作者： Fang Gao Qiang Zhao Gui-Xian Li School of Mechanical Electrical Engineering Harbin Institute of Technology Traffic College Northeast Forestry University

Continuum robot is a new type of biomimetic robot,which realizes the motion by bending some parts of its *** its path planning becomes more difficult even compared with hyper-redundant *** this paper a circular arc spline interpolating method is proposed for the robot shape description,and a new two-stage position-selectable-updating particle swarm optimization(TPPSO)algorithm is put forward to solve this path planning *** algorithm decomposes the standard PSO velocity’s single-step updating formula into twostage multi-point updating,specifically adopting three points as candidates and selecting the best one as the updated position in the first half stage,and similarly taking seven points as candidates and selecting the best one as the final position in the last half *** scheme refines and widens each particle’s searching trajectory,increases the updating speed of the individual best,and improves the converging speed and *** at the optimization objective to minimize the sum of all the motion displacements of every segmental points and all the axial stretching or contracting displacements of every segment,the TPPSO algorithm is used to solve the path planning *** detailed solution procedure is *** examples of five path planning cases show that the proposed algorithm is simple,robust,and efficient.

关键词： continuum robot path planning particle swarm optimization algorithm

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Multidirectional magnetic field decoupling model based on particle swarm optimization 21

Multidirectional magnetic field decoupling model based on Pa...

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IEEE 21st Biennial Conference on Electromagnetic Field Computation (IEEE CEFC)

作者： Zhang, Leran Shi, Minxia Yang, Jianzhi Shi, Ziyang Ma, Yuzheng Zhang, Ao Beihang Univ Sch Instrumentat Sci & Optoelect Engn Beijing Peoples R China

ISBN: (纸本)9798350348958;9798350348965

Active magnetic compensation is required to achieve near-zero magnetic environment for Magnetocardiography (MCG). The coupling relations between the magnetic fields of passive field cause difficulties in compensation. In this paper, we establish a multidirectional magnetic field decoupling model which build adjustive vector and unidirectional magnetic field matrix (AV-UMFM) equations. Based on particle swarm optimization (PSO) algorithm, the adjustive vector of the model can be acquired despite ill-conditioned problems. Finally, adjustive vector controls currents to simultaneously compensate for multidirectional residual magnetic fields and builds magnetic compensation system.

关键词： Magnetic field coupling magnetic field control particle swarm optimization algorithm

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A High-Dimensional Feature Selection Method via Selection and Non-selection Operators and Local Search Mechanism in particle swarm optimization 20th

A High-Dimensional Feature Selection Method via Selection an...

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20th International Conference on Intelligent Computing (ICIC)

作者： Zhu, Zhouming Li, Lingjie Xiao, Zhijiao Liu, Songbai Ma, Lijia Lin, Qiuzhen Ming, Zhong Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Guangdong Lab Artificial Intelligence & Digital E Shenzhen Peoples R China Shenzhen Technol Univ Coll Big Data & Internet Shenzhen Peoples R China

ISBN: (纸本)9789819755806;9789819755813

Recently, the problem of high-dimensional feature selection (FS) has become a current research focus in the field of evolutionary algorithms (EAs). However, most EA-based FS methods still face challenges in effectively combining relevance to remove redundant features, leading to low search efficiency and difficulty in finding high-quality feature subsets. To address this issue, this paper proposes a particle swarm optimization algorithm based on selection and non-selection operators and a local search mechanism, denoted as SNSLS-PSO. First, we design selection and non-selection operators based on Relief-F and roulette wheel selection to improve the quality of the selected feature subsets. Second, we introduce a local search mechanism based on an adaptive mutation operator, thereby avoiding local optima and enhance population diversity. In addition, we enhance the quality of particle selection by designing mutation probabilities for gene positions. The experimental results indicate that, compared to state-of-the-art FS methods, our proposed SNSLS-PSO can more efficiently select feature subsets and demonstrate superior classification performance on 14 high-dimensional datasets.

关键词： Feature selection high-dimensional datasets particle swarm optimization algorithm evolutionary algorithm

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Parameter identification of Hammerstein-Wiener nonlinear systems with unknown time delay based on the linear variable weight particle swarm optimization

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ISA TRANSACTIONS 2022年 120卷 89-98页

作者： Li, Junhong Zong, Tiancheng Lu, Guoping Nantong Univ Sch Elect Engn Nantong 226019 Peoples R China

This paper deals with the parameter estimation of Hammerstein-Wiener (H-W) nonlinear systems which have unknown time delay. The linear variable weight particle swarm method is formulated for such time delay systems. This algorithm transforms the nonlinear system identification issue into a function optimization issue in the parameter space, then utilizes the parallel searching ability of the particle swarm optimization and the iterative identification technique to realize the simultaneous estimation of all parameters and the unknown time delay. Finally, parameters in the linear submodule, nonlinear submodule and the time delay are separated from the optimum parameter. Moreover, two illustrative examples are exhibited to evaluate the effectiveness of the proposed method. The simulation results demonstrate that the derived method has fast convergence speed and high estimation accuracy for estimating H-W systems with unknown time delay, and it is applied to the identification of the bed temperature systems. (C) 2021 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Hammerstein-Wiener system Parameter estimation Time delay particle swarm optimization algorithm

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particle-swarm-optimization-Based 2D Output Feedback Robust Constraint Model Predictive Control for Batch Processes

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IEEE ACCESS 2022年 10卷 8409-8423页

作者： Zhang, Wangxi Ma, Jian Wang, Liming Jiang, Feng Hainan Normal Univ Sch Math & Stat Haikou 571158 Hainan Peoples R China Hainan Normal Univ Minist Educ Key Lab Data Sci & Intelligence Educ Haikou 571158 Hainan Peoples R China Key Lab Computat Sci & Applicat Hainan Prov Haikou 571158 Hainan Peoples R China Nanjing Univ Inst Philosophy Intelligence & Future Humanity Nanjing 210093 Peoples R China Nanjing Sport Inst Sch Phys Educ & Humanities Nanjing 210014 Peoples R China

For the input and output constraints and uncertainties in batch processes, a 2D output feedback robust constrained model predictive control (MPC) method is designed by combining iterative learning control (ILC), MPC and output feedback. Firstly, an equivalent 2D-FM closed-loop prediction model is established by combining with the proposed output feedback controller. Then an optimization performance index function with terminal constraint is constructed to study its control optimization. According to the designed optimization performance index and Lyapunov stability theory, the feasible MPC problem is obtained by solving the linear matrix inequalities (LMIs). At the same time, the gain of the new output feedback control law is given to ensure that the performance index reaches the minimum upper bound under the constraints of input and output. In order to solve the manual adjustment problem of some parameters in the performance index function, the particle swarm optimization (PSO) algorithm is introduced, and a better solution is found near the controller by using the search optimization method. Finally, taking the injection molding process as an example and comparing with the existing method without using PSO algorithms, it is proved that the above method is more feasible.

关键词： Output feedback Batch production systems Predictive control Performance analysis Uncertainty Prediction algorithms particle swarm optimization Batch processes input-output constraint iterative learning output feedback predictive control particle swarm optimization algorithm

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A particle swarm Optimizer with adaptive dynamic neighborhood for multimodal multi-objective optimization 35

A Particle Swarm Optimizer with adaptive dynamic neighborhoo...

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35th Chinese Control and Decision Conference (CCDC)

作者： Wei, Jingyue Zhang, Enze Ge, Rui Yangzhou Univ Informat Engn Coll Yangzhou Jiangsu Peoples R China

ISBN: (纸本)9798350334722

In this paper, a multi-objective particle swarm optimizer based on adaptive dynamic neighborhood (ADN-MOPSO) is proposed to locate multiple Pareto optimal solutions to solve multimodal multi-objective problems. In the proposed algorithm, a spatial distance-based non-overlapping ring topology is used to form multiple subpopulations for parallel search to enhance the local search capability of the algorithm. In addition, an adaptive dynamic neighborhood selection strategy is proposed to balance the exploration and exploitation capabilities of the algorithm, allowing the size of the subpopulation to change automatically when the neighborhood switch time is met. To prevent the algorithm from premature convergence, a stagnation detection strategy is introduced to apply a Gaussian perturbation operation to the particles that fall into the neighborhood optimum. Finally, the proposed algorithm is used to solve multimodal multi-objective test problems and compared with existing multimodal multi-objective optimization algorithms. The results show that the proposed algorithm can obtain more Pareto solutions when solving different types of multimodal multi-objective functions.

关键词： Multi-objective optimization multimodal multi-objective optimization particle swarm optimization algorithm sub-swarm regrouping ring topology

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