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IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)

作者： Yang Xiaojing Jiao Qingju Liu Xinke Anyang Normal Univ Coll Comp & Informat Engn Anyang 455000 Peoples R China

ISBN: (纸本)9781538662434

The linear decreasing weight particle swarm optimization algorithm (IDWPSO) is mentioned in the concept of a center particle, and then puts forward center particle swarm optimization algorithm (PSO). The linear decreasing weight particle swarm optimization algorithm, unlike other general center particle, particle velocity center is not clear, and is always placed in the center of the particle swarm. In addition, the neural network training algorithm compared to particle swarm optimization algorithm and the linear decreasing weight particle swarm optimization algorithm, results show that: the performance is better than the linear optimization center particle swarm decreasing weight PSO algorithm. algorithm.

关键词： particle swarm optimization algorithm neural network evolutionary computation

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A Novel Multiobjective particle swarm optimization algorithm with Dynamic Resource Allocation

A Novel Multiobjective Particle Swarm Optimization Algorithm...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Li, Lingjie Lin, Qiuzhen Wang, Jia Chen, Jianyong Ming, Zhong Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 510860 Guangdong Peoples R China

ISBN: (纸本)9781728121536

This paper proposes a novel multiobjective particle swarm optimization algorithm with dynamic resource allocation, showing promising performance especially for tackling some complicated multiobjective optimization problems. With the decomposition approach, each particle is assigned to optimize one subproblem with a novel velocity update strategy to speed up the convergence. Moreover, a dynamic resource allocation strategy is designed based on the relative improvement of subproblems, which can reasonably allocate computational resource to the particles that are able to search superior solutions. By this way, the proposed algorithm not only has strong exploratory capability, but also can converge quickly to the true Pareto-optimal front. The experimental results fully demonstrate the superiority of our proposed algorithm over four state-of-the-art multiobjective optimization algorithms, when tackling thirty-five test problems.

关键词： Multiobjective optimization particle swarm optimization algorithm Resource allocation strategy

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A greedy particle swarm optimization (GPSO) algorithm for testing real-world smart card applications

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INTERNATIONAL JOURNAL ON SOFTWARE TOOLS FOR TECHNOLOGY TRANSFER 2020年第2期22卷 183-194页

作者： Allawi, Hamzeh M. Al Manaseer, Waref Al Shraideh, Mohammad Univ Jordan Dept Comp Sci Amman Jordan

Software testing continues to be regarded as a necessary and critical step in the software development life cycle. Among the multitudes of existing techniques, particle swarm optimization (PSO) algorithm, in particular, has shown superior merits for automatically generating software test cases for its easy implementation and for relying on fewer parameters that require tuning. Hence, several state-of-the-art PSO-based algorithms have been successfully used as a test data generator. On the other hand, greedy-based algorithms, which are commonly used to solve complex and multi-step combinatorial problems, are starting to gain momentum as a solution for the complexity problem of software testing. Greedy algorithms favored over other techniques when the solution of the problem is guaranteed to be near-optimal. As a result, the utilization of both greedy and PSO algorithms in a single solution for automatically generating test data represents a strong candidate if designed carefully. In this paper, we propose a novel hybrid greedy and PSO algorithm (GPSO) that jointly guarantees the effectiveness and close to optimality results for generating a minimum number of test data. Compared with the widely employed genetic algorithm (GA), our proposed GPSO outperforms the GA in terms of the average number of iterations, execution time, and coverage percentage. Experimental trials with six different typical Java card applications show that the use of the proposed GPSO as a test data generator results in an outstanding performance.

关键词： Software testing Java smart card particle swarm optimization algorithm Greedy algorithm Genetic algorithm

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Determine the optimal switching angles symmetrical cascaded multilevel inverter using Improved particle swarm optimization algorithm

Determine the optimal switching angles symmetrical cascaded ...

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International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Sajjad Ahmadi Mostafa Andalib Sahnesaraie Saeid Hojjati Talami Fakhteh Dini Mahnaz Mohebbi Zanganeh Yazdan Ashgevari Department of electrical engineering Islamic Azad University Ardabil Iran Alborz Electric Power Distribution Company Alborz Iran Department of electrical engineering Islamic Azad University Mehriz Iran Faculty of electrical & Computer engineering Shahid Beheshti University Tehran Iran

ISBN: (数字)9781728131498

ISBN: (纸本)9781728131504

With the increasing use of inverters in the industry, the need to reduce the output harmonics is felt more than ever. Interstellar multi-level structures have grown exponentially due to benefits such as harmonic reduction and loss reduction. In multilevel inverters, nonlinear and complex equations become more complex when the number of surfaces increases. One of the ways to find optimal keying angles is to reduce the harmonics of the whole Newton-Raphson method. One of the main disadvantages of the Newton-Raphson method is the strong dependence of the answers to the initial guesses. By choosing different initial guesses, different answers may be obtained or results will not converge. And their solution is not possible by conventional methods of numerical solution of nonlinear equations such as the Newton-Ruffson method. In this thesis, one of a variety of Intelligent algorithms is used to solve the Newton-Raphson method problem. The particle swarm algorithm has been selected based on the proper background in solving problems of high complexity to solve the problem of optimal keying angles in this dissertation. In this thesis, the particle swarm algorithm has been improved. Simulation results for various multilevel converters have been presented and represent the high efficiency of the improved type of particle swarm algorithm in determining the appropriate angle of fire for reducing the high harmonics and generating a waveform with very low harmonic distortion and near sinusoidal.

关键词： Multilevel Inverters Selective Harmonic Elimination particle swarm optimization algorithm Improved particle swarm optimization algorithm particle swarm algorithm Inventors Newton-Raphson method Theses multilayers harmonics Angle Optimal keying loss reduction

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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 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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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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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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Finding a good shape parameter of RBF to solve PDEs based on the particle swarm optimization algorithm

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ALEXANDRIA ENGINEERING JOURNAL 2018年第4期57卷 3641-3652页

作者： Koupaei, Javad Alikhani Firouznia, Marjan Hosseini, Seyed Mohammad Mahdi Payame Noor Univ Dept Math Box 19395-3697 Tehran Iran Univ Khansar Dept Comp Engn Khansar Iran Shahid Bahonar Univ Kerman Dept Appl Math Kerman Iran

The present study aims at integrating the particle swarm optimization (PSO) algorithm with Kansa's method based on meshless collocation methods in order to determine a good shape parameter of Radial Basis Function (RBF) for solving partial differential equations (PDEs). For this purpose, we use a two-staged experimental design. While in the first stage, PSO algorithm was used to determine an optimal shape parameter for the related RBFs, in the second stage, we employed Kansa's method to estimate the RMS error for specifying approximate solutions. To study the performance of the proposed algorithm, we offer numerical results for two examples of partial differential equations and show the effectiveness of the proposed method. Numerical results demonstrated the performance superiority of the new algorithm model. The findings also indicated that the evolutionary algorithm model is more effective than the golden section search algorithm in finding a good shape parameter of RBF. (C) 2018 Faculty of Engineering, Alexandria University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： particle swarm optimization algorithm RBF Kansa's method PDEs Evolutionary algorithm

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