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Passive acoustic radiation control for a vibrating panel with piezoelectric shunt damping circuit using particle swarm optimization algorithm

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2009年第5期23卷 1446-1455页

作者： Jeon, Jin-Young Samsung Elect Co Ltd Digital Printing Div Digital Media & Commun Business Suwon 443742 South Korea

This paper presents a new acoustic radiation optimization method for a vibrating panel-like structure with a passive piezoelectric shunt damping system in order to minimize well-radiating modes generated from the panel. The optimization method is based on an idea of using the p-version finite element method(p-version FEM), the boundary element method(BEM), and the particle swarm optimization algorithm(PSOA). Optimum embossment design for the vibrating panel using the PSOA is first investigated in order to minimize noise radiation over a frequency range of interest. The optimum embossment design works as a kind of stiffener so that well-radiating natural modes are shifted up with some degrees. The optimized panel, however, may still require additional damping for attenuating the peak acoustic amplitudes. A passive shunt damping system is thus employed to additionally damp the well-radiating modes from the optimized panel. To numerically evaluate the acoustic multiple-mode damping capability by a shunt damping system, the integrated p-version FEM/BEM for the panel with the shunt damping system is modeled and developed by MATLAB. Using the PSOA, the optimization technique for the optimal multiple-mode shunt damper is investigated in order to achieve the optimum damping performance for the well-radiating modes simultaneously. Also, the acoustic damping performance of the shunt damping circuit in the acoustic environment is demonstrated numerically and experimentally with respect to the realistically sized panel. The simulated result shows a good agreement with that of the experimental result.

关键词： Shunt damping system P-version FEM BEM Sound power level particle swarm optimization algorithm

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GA-PSO Integration algorithm and Its Application in Modeling on Furnace Pressure System

GA-PSO Integration Algorithm and Its Application in Modeling...

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2016 2nd Workshop on Advanced Research and Technology in Industry Applications(WARTIA 2016)

作者： Qiming Chen Department of Automation North China Electric Power University

PSO algorithm is a kind of swarm intelligence optimization algorithm which has the advantages of simple principle,easy implementation,few parameters needed to adjust and so ***,the search accuracy of the basic PSO algorithm still needs to be *** this paper,a modified PSO algorithm using exponent decline inertia weight is put forward and successfully applied to the parameter identification of the furnace pressure *** modified PSO algorithm combines the nonlinear optimization and genetic algorithm to optimize the inertia weight and acceleration constants of the basic PSO algorithm,and is proved to be effective in parameter identification.

关键词： particle swarm optimization algorithm genetic algorithm system identification furnace pressure system

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Simultaneous lot-sizing and scheduling in flexible job shop problems

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2015年第1-4期78卷 1-18页

作者： Rohaninejad, Mohammad Kheirkhah, Amirsaman Fattahi, Parviz Bu Ali Sina Univ Dept Ind Engn Hamadan Iran

Procurement lot-sizing and production scheduling are as the two critical factors on controlling system costs. This paper considers a particular problem of integrated lot-sizing and scheduling for several products in capacitated flexible job shop configuration, taking into account sequence-dependent setup time. First, a novel mixed integer programming (MIP) model, based on big bucket time models, is proposed to formulate the problem. Then, in order to overcome the complexity of this model, a new hybrid algorithm which combines the genetic algorithm (GA), particle swarm optimization algorithm (PSO), and a local search heuristic is developed. The applicability of GA to solving problems with discrete variables and the efficacy of PSO to tackling problems with continuous variables is the motivation for applying the combination of these algorithms to the investigated problem which has both discrete and continuous solution space. The Taguchi method is used in order to calibrate the simulated annealing algorithm parameters. Then, the efficiency of the proposed algorithms is discussed. The computational results indicated that the proposed algorithm has performed better than the classic GA algorithm and MIP model with respect to both the quality of solutions and computation time.

关键词： Lot-sizing Scheduling Flexible job shop Genetic algorithm particle swarm optimization algorithm Shifting procedure

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Application of a two-stage fuzzy neural network to a prostate cancer prognosis system

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2015年第2期63卷 119-133页

作者： Kuo, Ren-Jieh Huang, Man-Hsin Cheng, Wei-Che Lin, Chih-Chieh Wu, Yung-Hung Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei 106 Taiwan Hon Hai Precis Ind Co Ltd New Taipei City 236 Taiwan Taipei Vet Gen Hosp Inst Clin Med Dept Urol Taipei 112 Taiwan Natl Yang Ming Univ Sch Med Dept Urol Taipei 112 Taiwan Taipei Vet Gen Hosp Superintendent Off Taipei 112 Taiwan

Objective: This study intends to develop a two-stage fuzzy neural network (FNN) for prognoses of prostate cancer. Methods: Due to the difficulty of making prognoses of prostate cancer, this study proposes a two-stage FNN for prediction. The initial membership function parameters of FNN are determined by cluster analysis. Then, an integration of the optimization version of an artificial immune network (Opt-aiNET) and a particle swarm optimization (PSO) algorithm is developed to investigate the relationship between the inputs and outputs. Results: The evaluation results for three benchmark functions show that the proposed two-stage FNN has better performance than the other algorithms. In addition, model evaluation results indicate that the proposed algorithm really can predict prognoses of prostate cancer more accurately. Conclusions: The proposed two-stage FNN is able to learn the relationship between the clinical features and the prognosis of prostate cancer. Once the clinical data are known, the prognosis of prostate cancer patient can be predicted. Furthermore, unlike artificial neural networks, it is much easier to interpret the training results of the proposed network since they are in the form of fuzzy IF THEN rules. These rules are very important for medical doctors. This can dramatically assist medical doctors to make decisions. (C) 2014 Elsevier B.V. All rights reserved.

关键词： optimization version of an artificial immune network particle swarm optimization algorithm Fuzzy neural network Prostate cancer Prognosis

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BIM-based integrated approach for detailed construction scheduling under resource constraints

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AUTOMATION IN CONSTRUCTION 2015年 53卷 29-43页

作者： Liu, Hexu Al-Hussein, Mohamed Lu, Ming Univ Alberta Dept Civil & Environm Engn Edmonton AB T6G 2W2 Canada

Building information modeling (BIM) has been recognized as an information technology with the potential to markedly change the Architecture, Engineering, and Construction (AEC) industry, and has drawn attention from numerous scholars within the construction domain. Despite the reported advancements pertaining to BIM in previous studies, the extended use of BIM has not yet reached its full potential. This paper thus presents a BIM-based integrated scheduling approach which facilitates the automatic generation of optimized activity-level construction schedules for building projects under resource constraints, by achieving an in-depth integration of BIM product models with work package information, process simulations, and optimization algorithms. A developed prototype system for panelized building construction as an add-on tool for Autodesk Revit is described. A case study is subsequently presented to demonstrate the methodology. Building on the existing body of research in this field, the key contribution of the proposed research is the in-depth integration of BIM product model with work package information, process simulations, and an optimization model, which provides solutions addressing the challenges of the existing practice with respect to detailed construction scheduling under resource constraints. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Building information modeling Integrated approach Work package information Process simulation model particle swarm optimization algorithm Detailed scheduling

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Nonlinear system identification using a cuckoo search optimized adaptive Hammerstein model

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第5期42卷 2538-2546页

作者： Gotmare, Akhilesh Patidar, Rohan George, Nithin V. Indian Inst Technol Dept Elect Engn Gandhinagar 382424 Gujarat India

An attempt has been made in this paper to model a nonlinear system using a Hammerstein model. The Hammerstein model considered in this paper is a functional link artificial neural network (FLANN) in cascade with an adaptive infinite impulse response (IIR) filter. In order to avoid local optima issues caused by conventional gradient descent training strategies, the model has been trained using a cuckoo search algorithm (CSA), which is a recently proposed stochastic algorithm. Modeling accuracy of the proposed scheme has been compared with that obtained using other popular evolutionary computing algorithms for the Hammerstein model. Enhanced modeling capability of the CSA based scheme is evident from the simulation results. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Hammerstein model System identification particle swarm optimization algorithm Differential evolution Cuckoo search algorithm

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Multiobjective optimization of process parameters for plastic injection molding via soft computing and grey correlation analysis

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2015年第1-4期78卷 525-536页

作者： Xu, Gang Yang, Zhitao Nanchang Univ Dept Math Nanchang 330031 Peoples R China S China Univ Technol Natl Engn Res Ctr Novel Equipment Polymer Proc Guangzhou 510641 Guangdong Peoples R China

In the plastic injection molding (PIM), the optimization of the process parameters is a complex task. The objective of this study is to propose an intelligent approach for efficiently optimizing PIM parameters when multiple objectives are involved, where different objectives, such as minimizing part weight, flash, or volumetric shrinkage, present trade-off behaviors. Multiple objective functions reflecting the product quality are constructed for the optimization model of PIM parameters. The proposed approach integrates Taguchi's parameter design method, back-propagation neural network (BPNN), grey correlation analysis (GCA), particle swarm optimization (PSO) and multiobjective particle swarm optimization (MOPSO) to locate the Pareto optimal solution for multiobjective optimization problem. PSO and GCA are applied to optimize the network structure of BPNN to establish multiobjective mathematical model (PSO-GCANN) that finely maps the relationship between the input process parameters and output multiresponse. MOPSO is used to fine-tune the Pareto optimal solutions while the approximate PSO-GCANN is utilized to efficiently compute the fitness of every individual during the evolution of MOPSO. The illustrative application and comparison of results show that the proposed methodology outperforms the existing methods and can help mold designers to efficiently and effectively identify optimal process parameters.

关键词： Plastic injection molding Back-propagation neural networks Grey correlation analysis particle swarm optimization algorithm Multiobjective optimization

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Study on Comprehensive optimization Method in the Performance of a New Type of High-speed USV and Its Analysis

Study on Comprehensive Optimization Method in the Performanc...

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2016 International Conference on Modeling, Simulation and optimization Technologies and Applications（MSOTA 2016）

作者： Zifan Wei Songlin Yang Shengping Jing Man Liu Jiangsu Modern Shipbuilding Technology Co Ltd Jiangsu University of Science and Technology

ISBN: (纸本)9781510835702

Taking the comprehensive performance of a new type of high-speed unmanned surface vehicle(USV) including its rapidity, maneuverability, seakeeping and overturning resistance into consideration, this paper establishes a mathematical model of the performance comprehensive optimization of high-speed USV. And according to the mathematical model and optimization algorithm, comprehensive optimization software based on a variety of optimization algorithms was designed and compiled to analyze the relevant calculation. The results indicates that the optimization effect of the growth mechanism is more superior to the roulette mechanism;genetic algorithm of growth mechanism and particle swarm optimization algorithm would have the best results in the optimization of the 5000 th generation, and it can get the rapidity optimization system had greater influence on the total optimization system;and what's more, the optimal values of hull length and other key design variables and partial parameters of different algorithms are obtained.

关键词： high-speed USV comprehensive optimization genetic algorithm particle swarm optimization algorithm key variables

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Determination of Rod's Position by the Shadow

Determination of Rod's Position by the Shadow

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2016 4th International Conference on Advanced Materials and Information Technology Processing(AMITP 2016)

作者： Saishuo Zheng School of North China Electric Power University

To find out the position of the rod with its shadow, a model for determining the position of the straight rod with the objective function of minimizing the error value is established. Some results had been obtained by particle swarm optimization algorithm, and were classified by the system cluster analysis to achieve the optimal results. After that, the optimal results were tested.

关键词： Shadow positioning particle swarm optimization algorithm Exhaustive method

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Bilevel and multi-objective dynamic construction site layout and security planning

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AUTOMATION IN CONSTRUCTION 2015年 57卷 1-16页

作者： Li, Zongmin Shen, Wenjing Xu, Jiuping Lev, Benjamin Sichuan Univ Sch Business Chengdu 610064 Peoples R China Sichuan Univ State Key Lab Hydraul & Mt River Engn Chengdu 610064 Peoples R China Drexel Univ Dept Decis Sci LeBow Coll Business Philadelphia PA 19104 USA

The joint problem of dynamic construction site layout and security planning is fundamental to any construction project. However, it is still largely unexplored. Few research studies investigated the implementation of security measures during the construction phase. This paper proposes a bilevel multi-objective model for the dynamic construction site layout and security planning problem. Specifically, the upper-level programming denotes that the project manager must first choose the construction site layout and security strategies to minimize the layout costs and consequences of a potential attack. The lower-level programming denotes that the attacker will destroy a subset of the facilities to inflict the maximum economic consequence on the construction facilities system. Thereafter, a bilevel multi-objective particle swarm optimization algorithm (MOBLPSO) is designed to solve this model. Finally, the approach is carried out in the Xiajiang hydropower large-scale construction project to illustrate the effectiveness of the proposed model and algorithm. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Dynamic construction site layout and security Bilevel multi-objective model particle swarm optimization algorithm

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