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Indoor positioning system based on particle swarm optimization algorithm

MEASUREMENT 2019年 134卷 908-913页

作者： Guo, Hang Li, Huixia Xiong, Jian Yu, Min Nanchang Univ Sch Resources Environm & Chem Engn Key Lab Poyang Lake Environm & Resource Utilizat Minist Educ Nanchang 330031 Jiangxi Peoples R China Nanchang Univ Nanchang 330031 Jiangxi Peoples R China Univ New Brunswick Dept Geodesy & Geomat Engn Fredericton NB Canada Jiangxi Normal Univ Nanchang 330022 Jiangxi Peoples R China

In recent years, wireless sensor networks localization becomes a crucial method in the indoor positioning. Following the frontiers of technology, we studied on ZigBee wireless sensor network. Since the parameters of the path loss model are difficult to be estimated by the ordinary methods, the particle swarm optimization (PSO) method is proposed in this paper to simulate the parameter estimation in the indoor environment. The Texas Instruments CC2530 chip was also taken to build a ZigBee wireless sensor network. The data collected from ZigBee wireless sensor network experiment could be used to estimate the model parameters. PSO algorithm for fitting the signal attenuation curve removed the poor experimental data, and the output model fit well with the signal attenuation curve. Experimental results demonstrate that the PSO algorithm works well, clear, easy to understand, and has a high reliability. Using the parametric model to locate the user's position, and with the weighted K-Nearest Neighbor algorithm, the two-dimensional (2D) positioning was improved effectively. The standard deviation of 2D positioning is 1.15 m, so the model has practical value. Through the experiment and analyzing the data, it is verified that the proposed PSO algorithm in this paper is better than the previous methods we presented. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： ZigBee Path loss model particle swarm optimization algorithm Parameter estimation K-Nearest Neighbor algorithm

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A Modified particle swarm optimization algorithm for the Design of a Double-Pad Aerostatic Bearing With a Pocketed Orifice-Type Restrictor

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JOURNAL OF TRIBOLOGY-TRANSACTIONS OF THE ASME 2014年第2期136卷 021701页

作者： Chang, S. H. Jeng, Y. R. Natl Chung Cheng Univ Dept Mech Engn Adv Inst Mfg High Tech Innovat Chiayi 62102 Taiwan

The performance of an aerostatic bearing with a pocketed orifice-type restrictor is affected by the bearing size, pocket size, orifice design, supply pressure, and bearing load. This study proposes a modified particle swarm optimization (MPSO) algorithm to optimize a double-pad aerostatic bearing. In bearing optimization, the upper and lower bearing designs are independent and several design variables that affect bearing performance must be considered. This study also applies the concept of mutation from a genetic algorithm. The results show that the MPSO algorithm has a global search capability and high efficiency to optimize a problem with several design variables and that the mutation can provide an avenue for particles to escape from a local optimal value.

关键词： aerostatic bearing particle swarm optimization algorithm orifice-type restrictor

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Combination of particle swarm optimization algorithm and artificial neural network to propose an efficient controller for vehicle handling in uncertain road conditions

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NEURAL COMPUTING & APPLICATIONS 2018年第2期30卷 585-593页

作者： Aalizadeh, B. Asnafi, A. Islamic Azad Univ Gachsaran Branch Dept Mech Engn Sama Tech & Vocat Training Coll Gachsaran Iran Shiraz Univ Hydroaeronaut Res Ctr Shiraz *** Iran

Due to fast variation of desired variables in vehicle handling problem, design of an accurate applicable economic and considerably quick responsible controller for steering control of such systems has attracted much attention in the literature. The problem becomes more complicated, if the variation of road condition comes into play also. In this study, by combination of a simple PID and an optimized adaptive neural network controller, an arrangement for active front steering control of vehicles in different road frictions is proposed. A general PID controller is picked up and then optimized using the particle swarm optimization algorithm. After that, a neural network is added consecutively and trained by the outputs of PID controller and neural network toolbox of MATLAB software. The proposed controller fulfills both the applicability and efficiency due to dual use of PID and neural network controllers. Simulation results confirm the rightness of suggested controller in active steering control of vehicles even for unpredictable road friction.

关键词： particle swarm optimization algorithm Artificial neural networks Vehicle handling Active front steering control

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Structural shape optimization by IGABEM and particle swarm optimization algorithm

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ENGINEERING ANALYSIS WITH BOUNDARY ELEMENTS 2018年 88卷 26-40页

作者： Sun, S. H. Yu, T. T. Nguyen, T. T. Atroshchenko, E. Bui, T. Q. Hohai Univ Dept Engn Mech Nanjing 211100 Jiangsu Peoples R China Univ Luxembourg Inst Computat Engn 6 Ave Fonte L-4362 Esch Sur Alzette Luxembourg Univ Chile Fac Phys & Math Sci Dept Mech Engn Santiago Chile Duy Tan Univ Inst Res & Dev Da Nang Vietnam Tokyo Inst Technol Dept Civil & Environm Engn Meguro Ku 2-12-1-W8-22 Tokyo 1528552 Japan

In this paper, a new approach is developed for structural shape optimization, which consists in coupling the particle swarm optimization (PSO) algorithm and the isogeometric boundary element method (IGA-BEM). The IGA-BEM is based on the combination of the isogeometric analysis (IGA) and the boundary element method (BEM), where Non-Uniform Rational B-Splines (NURBS) are employed as shape functions for geometry parameterization and approximation of the field variables. The method inherits the main advantage of the IGA-based shape optimization, i.e., the control points are used as design variables, thus the design model, analysis model and optimization model are uniformly described with the NURBS, providing easy communication between the three models and resulting in a smooth optimized boundary. However, the main feature of the proposed method is the use of PSO, which provides an attractive gradient-free alternative to complicated sensitivity analysis. The efficiency and accuracy of the proposed approach are demonstrated through four 2D shape optimization examples. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Shape optimization NURBS Isogeometric analysis Boundary element method particle swarm optimization algorithm

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A particle swarm optimization algorithm Based on Time-Space Weight for Helicopter Maritime Search and Rescue Decision-Making

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IEEE ACCESS 2020年 8卷 81526-81541页

作者： Chen, Zikun Liu, Hu Tian, Yongliang Wang, Rui Xiong, Peisen Wu, Guanghui Beihang Univ Sch Aeronaut Sci & Engn Beijing 100083 Peoples R China Commercial Aircraft Corp China Ltd Shanghai 200210 Peoples R China

One of the important problems to be solved in maritime search and rescue (MSAR) is decision-making, and the premise of it is determining the mission area for search and rescue unit. To solve the problem that classical cellular iterative search (CIS) algorithm is easy to fall into local optimal solution when determining the mission area, the particle swarm optimization algorithm based on time-space weight (TS-PSO) is proposed in this paper. This algorithm summarizes the optimization objectives and constraint conditions of the MSAR mission area planning according to search theory, carries out the parametric modeling of mission area legitimately and obtains the global optimal solution by continuous exploration in the parameter definition domain. On this basis, by analyzing the time-space weight of drift prediction data, the optimization results are further improved. Finally, through the case simulation analysis, it can be seen that the TS-PSO algorithm can effectively make up for the deficiency of the CIS algorithm and further improve the success probability of optimal MSAR mission area.

关键词： Global optimal solution maritime search and rescue mission area planning particle swarm optimization algorithm time-space weight

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An Improved particle swarm optimization algorithm for Bayesian Network Structure Learning via Local Information Constraint

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IEEE ACCESS 2021年 9卷 40963-40971页

作者： Liu, Kun Cui, Yani Ren, Jia Li, Peiran Hainan Univ Sch Informat & Commun Engn Haikou 570228 Hainan Peoples R China

At present, in the application of Bayesian network (BN) structure learning algorithm for structure learning, the network scale increases with the increase of number of nodes, resulting in a large scale of structure search space, which is difficult to calculate, and the existing learning algorithms are inefficient, making BN structure learning difficulty increase. To solve this problem, a BN structure optimization method based on local information is proposed. Firstly, it proposes to construct an initial network framework with local information and uses the Max-Min Parents and Children (MMPC) algorithm to construct an undirected network framework to reduce the search space. Then the particle swarm optimization (PSO) algorithm is used to strengthen the algorithm's optimization ability by constructing a new position and velocity update rule and improve the efficiency of the algorithm. Experimental results show that under the same sample data set, the algorithm can obtain a more accurate BN structure while converging quickly, which verifies the correctness and effectiveness of the algorithm.

关键词： Bayes methods Statistics Sociology Search problems particle swarm optimization Feature extraction Encoding Bayesian network structure learning local information particle swarm optimization algorithm

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A new feature selection method to improve the document clustering using particle swarm optimization algorithm

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JOURNAL OF COMPUTATIONAL SCIENCE 2018年 25卷 456-466页

作者： Abualigah, Laith Mohammad Khader, Ahamad Tajudin Hanandeh, Essam Said Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Zarqa Univ Dept Comp Informat Syst POB 13132 Zarqa Jordan

The large amount of text information on the Internet and in modern applications makes dealing with this volume of information complicated. The text clustering technique is an appropriate tool to deal with an enormous amount of text documents by grouping these documents into coherent groups. The document size decreases the effectiveness of the text clustering technique. Subsequently, text documents contain sparse and uninformative features (i.e., noisy, irrelevant, and unnecessary features), which affect the effectiveness of the text clustering technique. The feature selection technique is a primary unsupervised learning method employed to select the informative text features to create a new subset of a document's features. This method is used to increase the effectiveness of the underlying clustering algorithm. Recently, several complex optimization problems have been successfully solved using meta heuristic algorithms. This paper proposes a novel feature selection method, namely, feature selection method using the particle swarm optimization (PSO) algorithm (FSPSOTC) to solve the feature selection problem by creating a new subset of informative text features. This new subset of features can improve the performance of the text clustering technique and reduce the computational time. Experiments were conducted using six standard text datasets with several characteristics. These datasets are commonly used in the domain of the text clustering. The results revealed that the proposed method (FSPSOTC) enhanced the effectiveness of the text clustering technique by dealing with a new subset of informative features. The proposed method is compared with the other well-known algorithms i.e., feature selection method using a genetic algorithm to improve the text clustering (FSGATC), and feature selection method using the harmony search algorithm to improve the text clustering (FSHSTC) in the text feature selection. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Unsupervised feature selection Informative features particle swarm optimization algorithm K-mean text clustering algorithm

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Design a robust intelligent power controller for pressurized water reactor using particle swarm optimization algorithm

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KERNTECHNIK 2024年第5期89卷 690-698页

作者： Ateya, Afaf A. E. Saeed, Rehab M. Kandil, Magy M. Egyptian Atom Energy Author EAEA Cairo Egypt

Proportional-Integral-Derivative (PID) controllers have been optimized and used to overcome many types of problems in nuclear reactor systems. The high performance of PID controllers depend on optimizing their gains. In this research, an optimized robust PID controller is proposed to control power perturbations in a pressurized water reactor (PWR). The optimization process of robust PID using particle swarm optimization (PSO) algorithm aims to adapt PID gains then after that, H-infinity controller is used. The results show a good performance when that suggested hybrid controller is applied to the nuclear power system since the suggested design makes the system robust due to applying H-infinity method, in addition to get the benefits of the optimized PID controller.

关键词： pressurized water reactor proportional-integral-derivative controllers robust H-infinity controller particle swarm optimization algorithm

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

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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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An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem

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JOURNAL OF INTELLIGENT MANUFACTURING 2018年第3期29卷 603-615页

作者： Nouiri, Maroua Bekrar, Abdelghani Jemai, Abderezak Niar, Smail Ammari, Ahmed Chiheb Fac Sci Tunis Lab LIP2 Tunis Tunisia Carthage Univ INSAT Inst MMA Lab Tunis 1080 Tunisia King Abdulaziz Univ Fac Engn Dept Elect & Comp Engn Renewable Energy Grp Jeddah 21589 Saudi Arabia UVHC LAMIH Lab F-59313 Valenciennes France Univ Lille Nord France F-59000 Lille France

Flexible job-shop scheduling problem (FJSP) is very important in many research fields such as production management and combinatorial optimization. The FJSP problems cover two difficulties namely machine assignment problem and operation sequencing problem. In this paper, we apply particle swarm optimization (PSO) algorithm to solve this FJSP problem aiming to minimize the maximum completion time criterion. Various benchmark data taken from literature, varying from Partial FJSP and Total FJSP, are tested. Experimental results proved that the developed PSO is enough effective and efficient to solve the FJSP. Our other objective in this paper, is to study the distribution of the PSO-solving method for future implementation on embedded systems that can make decisions in real time according to the state of resources and any unplanned or unforeseen events. For this aim, two multi-agent based approaches are proposed and compared using different benchmark instances.

关键词： Flexible job-shop scheduling problem Makespan particle swarm optimization algorithm Routing and scheduling Multi-agent system Enmbeded system

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