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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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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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A wind power curtailment reduction strategy using electric vehicles based on individual differential evolution quantum particle swarm optimization algorithm

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ENERGY REPORTS 2022年 8卷 14578-14594页

作者： Zhang, Liang Yin, Qingbo Zhang, Zhihui Zhu, Zheng Lyu, Ling Hai, Koh Leong Cai, Guowei Northeast Elect Power Univ Key Lab Modern Power Syst Simulat & Control Renew Minist Educ Jilin 132012 Peoples R China State Grid Shanghai Energy Interconnect Res Inst Shanghai 201210 Peoples R China Global Energy Interconnect Dev & Cooperat Org Beijing 100006 Peoples R China Natl Metrol Ctr Singapore 138635 Singapore

A wind power curtailment consumption strategy using electric vehicles (EVs) based on individual differential evolution quantum particle swarm optimization algorithm (IDE-QPSO) is proposed, with the objective of reducing the system's wind curtailment in order to further improve the wind power consumption rate while effectively reducing wind power output fluctuation and amplitude. EV aggregators act as charging tariff setters, releasing dynamic time-of-use tariffs (DTOU) for EV clusters to respond to based on wind curtailment data accounted for by the dispatch center. This method first establishes an electric vehicle charging load model based on the travel chain theory and residents' travel rules, then establishes an EV users autonomous response model based on the sensitivity of electric vehicle users to the charging prices. Second, a multi-objective optimization function is established based on the aforementioned model, which integrates wind power curtailment consumption and minimizes wind power output fluctuation and amplitude, and it is solved using an improved quantum particle swarm optimization algorithm. Finally, adequate simulation experiments show that this strategy can effectively smooth out the fluctuation of wind power output and improve the wind power consumption rate. (C) 2022 The Author(s). Published by Elsevier Ltd.

关键词： Electric vehicle Reduce the wind curtailment Dynamic TOU tariffs Individual differential evolution quantum particle swarm optimization algorithm

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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 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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Indoor positioning system based on particle swarm optimization algorithm

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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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particle swarm optimization algorithm Based on Semantic Relations and Its Engineering Applications

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Systems Engineering Procedia 2012年 5卷 222-227页

作者： Liangshan Shao Yuan Bai Yunfei Qiu Zhanwei Du College of Science of Liaoning Technical University Liaoning China College of Computer Science and Technology Jilin University Changchun China

particle swarm optimization algorithm (PSO) is a good method to solve complex multi-stage decision problems. But this algorithm is easy to fall into the local minimum points and has slow convergence speed, According to the semantic relations, an improved PSO algorithm has been proposed in this paper. In contrast with the traditional algorithm, the improved algorithm is added with a new operator to update its crucial parameters. The new operator is to find out the potential semantic relations behind the history information based on the ontology technology. particle swarm optimization can be applied to many engineering fields, taking Traveling Salesman Problem (TSP) as example. Our experiments show accuracy of the improved particle swarm algorithm that is superior to that obtained by the other classical versions, and better than the results achieved by the compared algorithms, besides, this improved algorithm can also improve the searching efficiency.

关键词： particle swarm optimization algorithm semantic relation ontology technology engineering TSP

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particle swarm optimization algorithm-Based Design Method for Ultrasonic Transducers

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MICROMACHINES 2020年第8期11卷 715-715页

作者： Chen, Dongdong Zhao, Jianxin Fei, Chunlong Li, Di Zhu, Yuanbo Li, Zhaoxi Guo, Rong Lou, Lifei Feng, Wei Yang, Yintang Xidian Univ Sch Microelect Xian 710071 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China

In order to improve the fabrication efficiency and performance of an ultrasonic transducer (UT), a particle swarm optimization (PSO) algorithm-based design method was established and combined with an electrically equivalent circuit model. The relationship between the design and performance parameters of the UT is described by an electrically equivalent circuit model. Optimality criteria were established according to the desired performance;then, the design parameters were iteratively optimized using a PSO algorithm. The Pb(ZrxTi1-x)O-3(PZT) ceramic UT was designed by the proposed method to verify its effectiveness. A center frequency of 6 MHz and a bandwidth of -6 dB (70%) were the desired performance characteristics. The optimized thicknesses of the piezoelectric and matching layers were 255 mu m and 102 mu m. The experimental results agree with those determined by the equivalent circuit model, and the center frequency and -6 dB bandwidth of the fabricated UT were 6.3 MHz and 68.25%, respectively, which verifies the effectiveness of the developed optimization design method.

关键词： ultrasonic transducer equivalent circuit model optimization design particle swarm optimization algorithm

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An improved particle swarm optimization algorithm based on comparative judgment

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NATURAL COMPUTING 2018年第3期17卷 641-661页

作者： Wang, Chun-Feng Liu, Kui Henan Normal Univ Dept Math Xinxiang 453007 Peoples R China

particle swarm optimization (PSO) algorithm is one of the most effective and popular swarm intelligence algorithms. In this paper, based on comparative judgment, an improved particle swarm optimization (IPSO) is proposed. Firstly, a new search equation is developed by considering individual experience, social experience and the integration of individual and social experience, which can be used to improve the convergence speed of the algorithm. Secondly, in order to avoid falling into a local optima, a location abandoned mechanism is proposed;meanwhile, a new equation to generate a new position for the corresponding particle is proposed. The experimental results show that IPSO algorithm has excellent solution quality and convergence characteristic comparing to basic PSO algorithm and performs better than some state-of-the-art algorithms on almost all tested functions.

关键词： particle swarm optimization algorithm swarm intelligence Continuous optimization

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Application of a hybrid of genetic algorithm and particle swarm optimization algorithm for order clustering

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DECISION SUPPORT SYSTEMS 2010年第4期49卷 451-462页

作者： Kuo, R. J. Lin, L. M. Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei 106 Taiwan AU Optron Corp Hsinchu 300 Taiwan

This study proposes an evolutionary-based clustering algorithm based on a hybrid of genetic algorithm (GA) and particle swarm optimization algorithm (PSOA) for order clustering in order to reduce surface mount technology (SMT) setup time. Simulational results via Iris, Glass, Vowel and Wine benchmark data sets indicate that the proposed evolutionary-based clustering algorithm is more accurate than the GA-based and PSOA-based clustering algorithms. In addition, the model evaluation results which use order information provided by an international industrial personal computer (PC) manufacturer show that the proposed algorithm is also superior to GA-based and PSOA-based clustering algorithms. Through order clustering, scheduling orders that belong to the same cluster together can reduce production time as well as machine idle time. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Clustering analysis ART2 neural network particle swarm optimization algorithm Genetic algorithm

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