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A hybrid particle swarm optimization algorithm for load balancing of MDS on heterogeneous computing systems

NEUROCOMPUTING 2019年 330卷 380-393页

作者： Li, Dapu Li, Kenli Liang, Jie Ouyang, Aijia Hunan Univ Coll Informat Sci & Engn Changsha 410082 Hunan Peoples R China Zunyi Normal Univ Sch Informat Engn Zunyi 563006 Guizhou Peoples R China

An efficient hybrid genetic algorithm and particle swarm optimization algorithm (HGAPSO) is studied in this work for load balancing of molecular dynamics simulations (MDS) on heterogeneous supercomputers by combining the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm. A hybrid CPU-GPU platform is applied to enabling large-scale MDS that emulates the metal solidification. Applied to task scheduling of the parallel algorithm, the approach obtains excellent results. The experimental results show that the proposed algorithm can improve the efficiency of parallel computing and the precision of physical simulation. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Genetic algorithm Load balancing Metal solidification Molecular dynamics simulation particle swarm optimization algorithm Supercomputer

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Applying particle swarm optimization algorithm to roundness measurement

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EXPERT SYSTEMS WITH APPLICATIONS 2009年第2期36卷 3428-3438页

作者： Sun, Te-Hsiu Chaoyang Univ Technol Dept Ind Engn & Management Wufeng 413 Taichung County Taiwan

Measuring the roundness of a circular workpiece is a common problem of quality control and inspection. In this area, maximum inscribed circle (MIC) and maximum circumscribing circle (MCC), minimum zone circle (MZC) and least square circle (LSC) are four commonly used methods. In particular, MIC, MCC, and MZC, which are nonlinear constrained optimization problems, have not been thoroughly discussed lately. This study proposes a machine vision-based roundness measuring method that applies the particle swarm optimization algorithm (PSO) to compute MIC, MCC and MZC. To facilitate the PSO process, five different PSO's were encoded using a radius (R) and circle center (x,y) and extensively evaluated using an experimental design, in which the impact of inertia weight, maximum velocity and the number of particles on the performance of the particle swarm optimizer was analyzed. The proposed method was verified with a set of testing images and benchmarked with the GA-based (genetic algorithm) method [Chen, M. C. (2000). Roundness inspection strategies for machine visions using non-linear programs and genetic algorithms. International Journal of Production Research, 38, 2967-2988]. The experimental results reveal that the PSO-based method effectively solved the MIC, MCC, and MZC problems and outperforms GA-based method in both accuracy and the efficiency. As a finals, several industrial applications are presented to explore the effectiveness and efficiency of the proposed method. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Roundness measurement Machine vision particle swarm optimization algorithm

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Research on the coupling characteristics of energy power density of cutting vibration and cutting heat based on the particle swarm optimization algorithm

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TRANSACTIONS OF THE CANADIAN SOCIETY FOR MECHANICAL ENGINEERING 2022年第3期46卷 614-625页

作者： Li, Songyuan Li, Shuncai Li, Yuqing Vladimirovich, Petrov Anton Jiangsu Normal Univ Sch Mech & Elect Engn Xuzhou 221116 Jiangsu Peoples R China Jiangsu Normal Univ JSNU SPBPU Inst Engn Xuzhou 221116 Jiangsu Peoples R China Petersburg Polytech Univ Inst Adv Mfg Technol St Petersburg 1952151 Russia

Cutting heat and cutting vibration are important basic research topics in the field of machining. Many factors affect cutting heat and cutting vibration, and cutting heat and cutting vibration also affect each other. This papermainly studied the coupling characteristics between cutting vibration and cutting heat from the perspective of energy power density. A measurement system was built to collect the time-domain signals of cutting temperature and three-dimensional cutting vibration. Through Stefan-Boltzmann's law, the cutting thermal power density represented by the cutting temperature was obtained. Frequency domain analysis dealing with the self-power spectrum density was carried out on the three-dimensional vibration acceleration, and the operation of reducing the vibration dimension was carried out by principal component analysis. Based on the particle swarm optimization algorithm, two couplingmodels between cutting heat and cutting vibrationwere established. The research showed that the coupling correlation coefficient between cutting heat and cutting vibration was above 0.6. The coupling characteristics of cutting heat and cutting vibration were strong, and the impact of cutting vibration on cutting heat was more significant. The conclusions provide theoretical guidance for studying the coupling characteristics of cutting heat and cutting vibration from the energy perspective.

关键词： cutting heat cutting vibration coupling characteristics cutting energy power density particle swarm optimization algorithm

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Power optimization of gas pipelines via an improved particle swarm optimization algorithm

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Petroleum Science 2012年第1期9卷 89-92页

作者： Zheng Zhiwei Wu Changchun Beijing Key Laboratory of Urban Oil and Gas Distribution Technology China University of PetroleumBeijing 102249China

In past decades dynamic programming, genetic algorithms, ant colony optimization algorithms and some gradient algorithms have been applied to power optimization of gas pipelines. In this paper a power optimization model for gas pipelines is developed and an improved particle swarm optimization algorithm is applied. Based on the testing of the parameters involved in the algorithm which need to be defined artificially, the values of these parameters have been recommended which can make the algorithm reach efficiently the approximate optimum solution with required accuracy. Some examples have shown that the relative error of the particle swarm optimization over ant colony optimization and dynamic programming is less than 1% and the computation time is much less than that of ant colony optimization and dynamic programming.

关键词： Gas pipeline operation optimization particle swarm optimization algorithm

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Improved particle swarm optimization algorithm based novel encoding and decoding schemes for flexible job shop scheduling problem

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COMPUTERS & OPERATIONS RESEARCH 2020年 121卷 104951-104951页

作者： Ding, Haojie Gu, Xingsheng East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China

The flexible job shop scheduling problem (FJSP) is a typical scheduling problem in practical production and has been proven to be a NP-hard problem. The study of FJSP is important to remarkably direct actual manufacturing processes. The paper proposes an improved particle swarm optimization (PSO) algorithm for solving FJSP and obtains beneficial solutions by improvement on encoding/decoding scheme, communication mechanism between particles, and alternate rules of candidate machines of operations. The innovation of encoding/decoding scheme proposes a novel designed chain encoding scheme and a corresponding effective decoding scheme. The chain-based encoding scheme can reasonably convert FJSP to an appropriate operation linked list and the novel designed decoding scheme owns the capacity of further explorering the solution space. The improvement of traditional PSO focuses on the innovation of information communication between particles, besides the modification of algorithm architecture. The amelioration of rules on operated machine selection is carried out based on the critical path of operations research (OR). It promotes algorithm efficiency by only alternating the candidate machines of operations on the critical path. In addition, much parameters tuning work is involved in a series of experiments. The study proposes some tuning schemes of parameters with exact mathematical methods, and these schemes can effectively help find more appropriate parameters. The final experiment results prove that the improved PSO exhibits remarkable ability to solve FJSP. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Flexible job shop scheduling problem particle swarm optimization algorithm Encoding and decoding schemes Local search Operations research

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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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Structure optimization of electromagnetic clinching die based on response surface methodology and particle swarm optimization algorithm

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2023年第3-4期126卷 1611-1628页

作者： Ji, Yanan Huang, Changqing Sun, Xiaoming Wang, Shu Yang, Huan Cui, Xiaohui Cent South Univ Light Alloy Res Inst Changsha 410083 Peoples R China Cent South Univ State Key Lab High Performance Complex Mfg Changsha 410083 Peoples R China Cent South Univ Coll Mech & Elect Engn Changsha 410083 Peoples R China

Electromagnetic clinching (EMC) is a high-speed connection technology that combines electromagnetic forming and mechanical clinching. The die structure significantly affects the mechanical properties of the joint. In this paper, an optimal method based on response surface methodology (RSM) and particle swarm optimization algorithm (PSO) is proposed to improve the mechanical properties of joints clinched by EMC. The mathematical models of die geometrical parameters and cross-sectional parameters of the joint are obtained by RSM and numerical simulation. Then, the die structure is optimized through PSO. Finally, the experiments are carried out using the optimized and standardized die. The results show small errors between the simulation and the experiment. The joint strength using a standardized die is 878 N, and energy absorption is 0.758 J. The joint strength using the optimized die is 1322 N, and energy absorption is 1.335 J. Compared with the standardized die, the joint strength and energy absorption of parts obtained by the optimized die are increased by 50.5% and 76.1%, respectively.

关键词： Electromagnetic clinching Die structure optimization Response surface methodology particle swarm optimization algorithm

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A particle swarm optimization algorithm for optimization of thermal performance of a smooth flat plate solar air heater

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ENERGY 2012年第1期38卷 406-413页

作者： Siddhartha Sharma, Naveen Varun Natl Inst Technol Dept Mech Engn Hamirpur 177005 India

This work is undertaken with an objective to develop and implement a trained particle swarm optimization (PSO) algorithm for prediction of an optimized set of design and operating parameters for a smooth flat plate solar air heater (SFPSAH). The simulation is carried out based on the basis of the algorithm developed for three different cases using the climatic condition data of the city Hamirpur, India situated between (latitude) 31 degrees 25'-31 degrees 52'N and (longitude) 76 degrees 18' to 76 degrees 44' E. The final results obtained from this algorithm are compared with experimental results and found to be satisfactory as far as flexibility, speed and global convergence are concerned. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Thermal performance Solar air heater particle swarm optimization algorithm

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Load balancing in virtual machines of cloud environments using two-level particle swarm optimization algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第6期45卷 9433-9444页

作者： Zhou, Chunrong Jiang, Zhenghong ChongqingVocat Coll Transportat Sch Big Data Chongqing 402247 Peoples R China

Load balancing in cloud computing refers to dividing computing characteristics and workloads. Distributing resources among servers, networks, or computers enables enterprises to manage workload demands. This paper proposes a novel load-balancing method based on the Two-Level particle swarm optimization (TLPSO). The proposed TLPSO-based load-balancing method can effectively solve the problem of dynamic load-balancing in cloud computing, as it can quickly and accurately adjust the computing resource distribution in order to optimize the system performance. The upper level aims to improve the population's diversity and escape from the local optimum. The lower level enhances the rate of population convergence to the global optimum while obtaining feasible solutions. Moreover, the lower level optimizes the solution search process by increasing the convergence speed and improving the quality of solutions. According to the simulation results, TLPSO beats other methods regarding resource utilization, makespan, and average waiting time.

关键词： Load balancing cloud computing virtualization particle swarm optimization algorithm

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Optical watermark scheme based on singular value decomposition ghost imaging and particle swarm optimization algorithm

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JOURNAL OF MODERN OPTICS 2020年第12期67卷 1059-1071页

作者： Wu, Jun-Yun Huang, Wei-Liang Zuo, Mei-Juan Gong, Li-Hua Nanchang Univ Dept Comp Sci & Technol Nanchang Jiangxi Peoples R China Nanchang Univ Dept Elect Informat Engn Nanchang 330031 Jiangxi Peoples R China

An optical image watermarking algorithm, based on singular value decomposition (SVD) ghost imaging and multiple transforms, is designed. The watermark image is first encrypted by applying an SVD ghost imaging system, then the encrypted watermark is embedded into the cover image with the help of multiple transforms, including lifting wavelet transform (LWT), discrete cosine transform (DCT), discrete fractional angular transform (DFAT) and SVD. Four sub-band images are produced from the host image by LWT and DCT. The improved DFAT, whose scaling factors and parameter are optimized by particle swarm optimization algorithm, is operated in the new matrix. Afterwards, SVD is executed in the two-part image and the encrypted watermark is embedded in the host image by mutual operation of different matrices. Simulation results validate that the proposed watermark scheme is superior in the aspects of security, robustness and imperceptibility.

关键词： Optical watermarking SVD ghost imaging lifting wavelet transform discrete fractional angular transform particle swarm optimization algorithm

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