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检索条件"主题词=particle Swarm Optimization algorithm"

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Data Classification Based on the Hybrid Versions of the particle swarm optimization algorithm 7

Data Classification Based on the Hybrid Versions of the Part...

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7th Mediterranean Conference on Embedded Computing (MECO)

作者： Demidova, Liliya Klyueva, Irina Ryazan State Radio Engn Univ Ryazan Russia

ISBN: (纸本)9781538656839

The paper presents two hybrid versions of the basic PSO algorithm, involving the use of the classical Grid Search (GS) algorithm and Design of Experiment (DOE) algorithm correspondingly. These hybrid versions have been applied to the problem of search of the parameters values of the SVM classifier. The results of experimental studies confirm the application efficiency of the hybrid versions of the basic PSO algorithm with the aim of reducing of the time expenditures for searching the optimum parameters of the SVM classifier while maintaining of high quality of its classification decisions.

关键词： classification particle swarm optimization algorithm grid search algorithm SVM classifier radial basis kernel function

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Application of Neural Network and particle swarm optimization algorithm in Slope Runoff and Sediment Yield Calculation

Application of Neural Network and Particle Swarm Optimizatio...

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2nd International Conference on Manufacturing Science and Engineering

作者： Huang Jun Wu Pute Zhao Xining Wang Juan NW A&F Univ Coll Water Resources & Architecture Engn Yangling 712100 Shaanxi Peoples R China

ISBN: (纸本)9783037850398

Neural network black box model for predicting the slope runoff and sediment yield and two empirical equations for calculating the slope runoff and sediment yield were established with the basis of practical field data of slope runoff and sediment amount by artificial simulated rainfall experiments. In additional, particle swarm optimization algorithm is used to inquire the empirical equation's unknown parameters based on least square method. And results show that, neural network model might represent the nonlinear relationship between runoff, sediment amount and each impact factor excellently. Furthermore, predicted results are satisfactory and its relative error mean is around 10%. Empirical equations are reasonably and reliable, its relative error mean is less than 20%. These two methods provide an operable means for such intricate research of slope runoff and sediment yield predication and calculation.

关键词： Artificial Simulated Rainfall Runoff Amount Sediment Amount Neural Network particle swarm optimization algorithm Least Square Method

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Two Sub-swarms Quantum-behaved particle swarm optimization algorithm Based on Exchange Strategy

Two Sub-swarms Quantum-behaved Particle Swarm Optimization A...

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3rd International Symposium on Intelligent Information Technology and Security Informatics

作者： Liu, Zhihua Sun, Hui Hu, Haizhi NanChang Hang Kong Univ Sch Software Nanchang Jiangxi Peoples R China NanChang Inst Technol Dept Comp Sci & Technol Nanchang Jiangxi Peoples R China NanChang Hang Kong Univ Sch Informat Engn Nanchang Jiangxi Peoples R China

ISBN: (纸本)9780769540207

In this paper, a Two Sub-swarms Quantum-behaved particle swarm optimization algorithm Based on Exchange Strategy (TS-QPSO) is proposed. Two sub-swarms of particles with quantum Behavior are set up in TS-QPSO. Once the whole swarm falls into local optima and the best value of the global swarm is not improved after the allowable iterations, the exchange strategy will be carried out. The amount of exchange particles is different in each searching phase. In this way, the population diversity can be improved greatly and the problem that falling into local optima can be avoided effectively. Experiment results show that the overall performance of TS-QPSO is superior to QPSO algorithm and TSPSO algorithm.

关键词： particle swarm optimization algorithm local optima Quantum-behaved particle swarm optimization algorithm exchange strategy

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A particle swarm optimization algorithm Based on Molecule Diffusion

A Particle Swarm Optimization Algorithm Based on Molecule Di...

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International Conference on Industrial Mechatronics and Automation

作者： Liu, Xiaoxiang Jiang, Weigang Xie, Jianwen Jinan Univ Zhuhai Coll Dept Comp Sci Zhuhai Peoples R China

ISBN: (纸本)9781424438181

Inspired by the diffusion movement phenomenon of the molecule, a molecule-diffusion particle swarm optimization (MDPSO) is presented. The proposed algorithm (MDPSO) has attraction and diffusion phases. Once the diversity of population become low, the individuals will be dispersed and turn into diffusion phases, while if the diversity of population get high, the individuals carry out the attraction phases. It is indicated that MDPSO not only prevents premature convergence to a high degree, but also keeps a more rapid convergence rate than SPSO by applying MDPSO to portfolio problem and comparing with SPSO and other algorithms.

关键词： molecule diffusion particle swarm optimization algorithm portfolio selection

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Weighted particle swarm optimization algorithm for Randomized Unit Testing 1

Weighted Particle Swarm Optimization Algorithm for Randomize...

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IEEE International Conference on Electrical, Computer and Communication Technologies

作者： Dhivya, K. Devika Rani Meenakshi, V. S. Sri Krishna Arts & Sci Coll Dept CA & SS Coimbatore Tamil Nadu India Dr Mahalingam Coll Engn & Technol Dept Comp Applicat Pollachi India

ISBN: (纸本)9781479960859

Randomized testing is an effective method for testing software units. Thoroughness of randomized unit testing is according to the settings of optimal parameters. Randomized testing uses randomization for some aspects of test input data. Designing Genetic algorithm (GA) is somewhat of a black art. The feature subset selection (FSS) tool is used with GA to assess and to reduce the size and the content of the test case. FSS can be used to find and remove unnecessary parts of the search control automatically. The existing system does not cover all test data in test cases for the reason that it can quickly generate many test cases and does not consider the target method. Thus GA for Randomized unit testing has not achieves high coverage and does not produce better optimal test data. In the proposed method, particle swarm optimization (PSO) algorithm is used for randomized unit testing. PSO algorithm is used to evaluate the target method solutions for test coverage in test data. The main goal is to generate the optimal test parameter, to reduce the size of test case generation and to achieve high coverage of the units under test. PSO achieves high coverage and produce optimal value. PSO algorithm is enhanced weighted value. Weighted particle swarm optimization (WPSO) algorithm uses weight value in calculating the mean best position for each particle. It improves the efficiency of the system and achieves high coverage of the units under test within 5% of the time with better accuracy.

关键词： Randomized unit testing Genetic algorithm Feature Sub Set Selection particle swarm optimization algorithm Weighted particle swarm optimization algorithm

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An Assimilated Approach of Concept Analysis and particle swarm optimization algorithm for Effective Test Suite Minimization

An Assimilated Approach of Concept Analysis and Particle Swa...

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IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)

作者： Selvakumar, S. Manikumar, T. Kumar, A. John Sanjeev Latha, L. GKM Coll Engn Technol Dept Comp Sci & Engn Madras Tamil Nadu India RVS Coll Engn Technol Dept Comp Applicat Dindigul India Thiagarajar Coll Engn Dept Comp Applicat Madurai 625015 Tamil Nadu India Bharathiar Univ Dept Comp Sci Ctr Res & Dev Coimbatore Tamil Nadu India

ISBN: (纸本)9781479915941;9781479915958

In most of the test suite minimization techniques, either the size minimization is more or the fault detection is more. But a combination of both would yield better qualified reduced test suite. This paper presents a technique where the size minimization is obtained through the optimization algorithm, particle swarm optimization and the Fault Detection Effectiveness is obtained through Concept Analysis. In spite of our algorithm producing results similar to Genetic algorithm, the computation time of our algorithm is simple and improves the fault detection capacity. The experimental results indicate that PSO outperforms GAs for most code elements to be covered in terms of effectiveness and efficiency.

关键词： Test suite minimization concept analysis particle swarm optimization algorithm Empirical analysis

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An improved multi-objective particle swarm optimization algorithm and its application in vehicle scheduling

An improved multi-objective particle swarm optimization algo...

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Chinese Automation Congress (CAC)

作者： Xu, Wenxing Wang, Wanhong He, Qian Liu, Cai Zhuang, Jun Beijing Inst Petrochem Technol Dept Automat Beijing Peoples R China SUNY Buffalo Dept Ind & Syst Engn Buffalo NY USA

ISBN: (纸本)9781538635247

Due to the lack of diversity of the initial population, the multi-objective particle swarm optimization algorithm easily falls into the local optimal value during the iterative process. The method of piecewise logistic chaotic map is introduced to increase the randomness of initial population. A disturbance variable is used to weaken the dependency on global optimal value. A segmented maintenance of the external file is used to select the particle which is more representative for the population. A monitoring selection mechanism is used to improve the population jump out of local optimum. The strategy for eliminating the final particle one by one is used to clip the external file. The validity of the proposed algorithm is proved by comparing with the other algorithms on the test function. And the proposed algorithm has been used to solve the vehicle routing problem.

关键词： multi-objective optimization particle swarm optimization algorithm diversity vehicle scheduling

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AN IMPROVED ADAPTIVE PROPAGATION CHAOTIC particle swarm optimization algorithm BASED ON IMMUNE SELECTION 16

AN IMPROVED ADAPTIVE PROPAGATION CHAOTIC PARTICLE SWARM OPTI...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Huang, Ke Zhang, Haolan Wang, Yiwen Yu, Changbin Zhejiang Univ Ningbo Inst Technol Ningbo 315100 Zhejiang Peoples R China

ISBN: (纸本)9781538604083

The particle swarm optimization algorithm is improved by introducing the immune selection, adaptive propagation, multi-population evolution. An improved adaptive propagation chaotic particle swarm optimization algorithm based on immune selection (IS-APCPSO algorithm for short) is proposed in this paper. The performance of several algorithms has been compared by a classic example of traffic network optimization. It is proved that the improved algorithm in accelerating convergence rate, increasing the diversity of particles, and preventing premature phenomenon is effective. The novel algorithm is expected to be used in the model solution of large-scale complex traffic network optimization problem.

关键词： particle swarm optimization algorithm Immune selection Adaptive propagation Multi-population evolution Traffic network optimization

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Dynamic particle swarm optimization algorithm based Maximum Power Point Tracking of Solar Photovoltaic Panels

Dynamic Particle Swarm Optimization Algorithm based Maximum ...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Huynh, Duy C. Nguyen, Thoai N. Dunnigan, Matthew W. Mueller, Markus A. Vietnam Natl Univ Univ Technol Power Syst Dept Ho Chi Minh City Vietnam

ISBN: (纸本)9781467351942;9781467351928

This paper proposes a novel application of a dynamic particle swarm optimization (PSO) algorithm for determining a maximum power point (MPP) of a solar photovoltaic (PV) panel. Solar PV cells have a non-linear V-I characteristic with a distinct MPP which depends on environmental factors such as temperature and irradiation. In order to continuously harvest maximum power from the solar PV panel, it always has to be operated at its MPP. The proposed dynamic PSO algorithm is one of the PSO algorithm variants, which modifies the acceleration coefficients of the cognitive and social components in the velocity update equation of the PSO algorithm as linear time-varying parameters to improve the global search capability of particles in the early stage of the optimization process and direct the global optima at the end stage. The obtained simulation results are compared with MPPs achieved using other algorithms such as the standard PSO, and Perturbation and Observation (P&O) algorithms under various atmospheric conditions. The results show that the dynamic PSO algorithm is better than the standard PSO and P&O algorithms for determining and tracking MPPs of solar PV panels.

关键词： solar photovoltaic panel maximum power point tracking particle swarm optimization algorithm

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Improved particle swarm optimization algorithm for optimum charge plan for steelmaking-continuous casting production scheduling

Improved particle swarm optimization algorithm for optimum c...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Jian, W Xuc, YC Huang, J Hubei Automot Ind Inst Dept Elect Engn Shiyan Peoples R China

ISBN: (纸本)0780386620

An optimum furnace charge plan model for steelmaking continuous casting planning and scheduling is presented. An improved particle swarm optimization is presented to solve the optimum charge plan problem. Simulations have been carried and the results show that the improved PSO has good performance than the standard PSO. This improved PSO has been used to solve the optimum charge plan problem. The computation with practical data shows that the model and the solving method are very effective.

关键词： particle swarm optimization algorithm charge plan steelmaking process modeling simulation

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