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High utility-itemset mining and privacy-preserving utility mining

Perspectives in Science 2016年 7卷 74-80页

作者： Jerry Chun-Wei Lin Wensheng Gan Philippe Fournier-Viger Lu Yang Qiankun Liu Jaroslav Frnda Lukas Sevcik Miroslav Voznak School of Computer Science and Technology Harbin Institute of Technology Shenzhen Graduate School Shenzhen China School of Natural Sciences and Humanities Harbin Institute of Technology Shenzhen Graduate School Shenzhen China Department of Telecommunications Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava 17. listopadu 15 708 00 Ostrava-Poruba Czech Republic

In recent decades, high-utility itemset mining (HUIM) has emerging a critical research topic since the quantity and profit factors are both concerned to mine the high-utility itemsets (HUIs). Generally, data mining is commonly used to discover interesting and useful knowledge from massive data. It may, however, lead to privacy threats if private or secure information (e.g., HUIs) are published in the public place or misused. In this paper, we focus on the issues of HUIM and privacy-preserving utility mining (PPUM), and present two evolutionary algorithms to respectively mine HUIs and hide the sensitive high-utility itemsets in PPUM. Extensive experiments showed that the two proposed models for the applications of HUIM and PPUM can not only generate the high quality profitable itemsets according to the user-specified minimum utility threshold, but also enable the capability of privacy preserving for private or secure information (e.g., HUIs) in real-word applications.

关键词： Data mining High-utility itemset Privacy preserving pso algorithm GA algorithm Evolutionary algorithms

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A new approach to particle swarm optimization algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第2期42卷 844-854页

作者： Gosciniak, Ireneusz Univ Silesia Inst Comp Sci PL-41200 Sosnowiec Poland

Particularly interesting group consists of algorithms that implement co-evolution or co-operation in natural environments, giving much more powerful implementations. The main aim is to obtain the algorithm which operation is not influenced by the environment. An unusual look at optimization algorithms made it possible to develop a new algorithm and its metaphors define for two groups of algorithms. These studies concern the particle swarm optimization algorithm as a model of predator and prey. New properties of the algorithm resulting from the co-operation mechanism that determines the operation of algorithm and significantly reduces environmental influence have been shown. Definitions of functions of behavior scenarios give new feature of the algorithm. This feature allows self controlling the optimization process. This approach can be successfully used in computer games. Properties of the new algorithm make it worth of interest, practical application and further research on its development. This study can be also an inspiration to search other solutions that implementing co-operation or co-evolution. (C) 2014 Published by Elsevier Ltd.

关键词： Co-evolutionary systems pso algorithm Predator-prey algorithm Immune algorithm Optimization method Games Artificial intelligence Entropy Multifractal analysis

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Particle Swarm Optimization algorithm for Leakage Power Reduction in VLSI Circuits

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INTERNATIONAL JOURNAL OF ELECTRONICS AND TELECOMMUNICATIONS 2016年第2期62卷 179-186页

作者： Rani, V. Leela Latha, M. Madhavi GVP Coll Engn Autonomous Dept ECE Visakhapatnam AP India JNTUH Coll Engn Dept ECE Hyderabad Telangana India

Leakage power is the dominant source of power dissipation in nanometer technology. As per the International Technology Roadmap for Semiconductors (ITRS) static power dominates dynamic power with the advancement in technology. One of the well-known techniques used for leakage reduction is Input Vector Control (IVC). Due to stacking effect in IVC, it gives less leakage for the Minimum Leakage Vector (MLV) applied at inputs of test circuit. This paper introduces Particle Swarm Optimization (pso) algorithm to the field of VLSI to find minimum leakage vector. Another optimization algorithm called Genetic algorithm (GA) is also implemented to search MLV and compared with pso in terms of number of iterations. The proposed approach is validated by simulating few test circuits. Both GA and pso algorithms are implemented in Verilog HDL and the simulations are carried out using Xilinx 9.2i. From the simulation results it is found that pso based approach is best in finding MLV compared to Genetic based implementation as pso technique uses less runtime compared to GA. To the best of the author's knowledge pso algorithm is used in IVC technique to optimize power for the first time and it is quite successful in searching MLV.

关键词： Leakage power pso algorithm Genetic algorithm Minimum leakage vector Verilog HDL implementation

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OPTIMIZATION OF THREE-PHASE PWM RECTIFIER ROBUST CONTROL SYSTEM BASED ON IMPROVED pso algorithm

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INTELLIGENT AUTOMATION AND SOFT COMPUTING 2011年第7期17卷 847-858页

作者： Tan, Guojun Zhang, Hui Qian, Miaowang Yu, Ruiwen China Univ Min & Technol Sch Informat & Elect Engn Xuzhou Peoples R China

This paper proposes a robust H-infinity control method for three-phase PWM rectifier. Based on the mathematical model of the PWM rectifier, the state equations which include outside interference are established for the standard setup I-I. control problem with suitable weighting functions. The robust H-infinity controller can be derived by solving the Riccati inequality. An improved pso algorithm is proposed in this paper by bringing the ideas of SA algorithm and chaotic search into the pso algorithm. And it is used to optimize the parameters of proposed controller. Simulation and experiment results verify that the designed robust H-infinity controller has a better external disturbance rejection capability compared with the conventional PI controller. And the improved pso algorithm has ability to determine the parameters effectively for the proposed controller.

关键词： robust H-infinity control PWM rectifier pso algorithm

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An improved strategy of pso for solving multimodal and higher dimensional complicated optimization problems 7

An improved strategy of PSO for solving multimodal and highe...

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7th International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Du, Shuxing Cui, Jiashan Xidian Univ Sch Mechanoelect Engn Xian Peoples R China Xidian Univ Line Chool Mechanoelect Engn Xian Peoples R China

ISBN: (纸本)9781509000760

Particle Swarm Optimization (pso) is an evolutionnary computation technique. Separate adjustment to inertia weight and learning factors in pso undermines the integrity and intelligent characteristic in the evolutionary process of particle swarm to some extent, thus it is not suitable for solving most complicated optimization problems. On the basis of previous researches, the aim of this study was to improve the computational efficiency of pso and avoid premature convergence for multimodal, higher dimensional complicated optimization problems by considering the mutual influences of inertia weight and learning factors on the updates of particle's velocities. A great number of experiment data provided evidence that the nonlinear adjustment to inertia weight, cognitive learning factor and social learning factor within a certain interval is a good selection. Moreover, the simulation results on four typical functions show that the improved strategy of pso proposed in the paper is available for solving multimodal and higher dimensional complicated optimization problems, and can accelerate convergence speed, improve optimization quality effectively in comparison to the algorithms of standard pso and existing relevant improved pso.

关键词： pso algorithm inertia weight learning factor multimodal function higher dimensional function optimization performance simulation test

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Optimal Siting and Sizing of Stationary Supercapacitors in a Metro Network using pso

Optimal Siting and Sizing of Stationary Supercapacitors in a...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Calderaro, Vito Galdi, Vincenzo Graber, Giuseppe Piccolo, Antonio Univ Salerno Dept Ind Engn Fisciano SA Italy

ISBN: (纸本)9781479978007

In this paper, we investigate the design of stationary ESSs based on supercapacitors (SCs) for metro network (MN). We implement a simulation tool in order to estimate the power flow among the metro vehicles and the ESSs through the MN. A new formulation of the ESSs siting and sizing optimisation problem is proposed and solved using particle swarm algorithm. The optimisation process minimises the energy supplied by the electrical substations and the whole ESSs capacity taking into the electrical line constraints in the metro network simulator. Several simulations are performed in order to design the number, the position along the track and the required capacity of the SCs. Using commercially available modules, a realistic design solution is compared with the theoretical one obtained by solving the optimisation algorithm. Finally, we estimate the energy delivered from the electric substations and the energy saved using the real ESSs configuration.

关键词： metro network regenerative braking energy storage siting sizing supercapacitors pso algorithm optimisation

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pso Based UAV Online Path Planning algorithms 2017

PSO Based UAV Online Path Planning Algorithms

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International Conference on Automation, Control and Robots (ICACR)

作者： Ou, Xiaoyu Liu, Yang Zhao, Yijing Vocat Educ Ctr Chongqing Peoples R China Beihang Univ Dept Automat Control Beijing Peoples R China

ISBN: (纸本)9781450363501

Path planning algorithms obtain an available flight path with threat avoidance independently. In this paper, an improved particle swarm optimization algorithm is proposed and applied to the real-time path planning of UAV based on the analysis of particle swarm optimization. The Chaos strategy prevents particles from getting into a local optimal, and Dijkstra algorithm improves the quality of the path. In static mode, the experimental comparison between paths generated by classical particle algorithm and improved particle swarm algorithm proves that improvement strategy is effective. Path planning algorithms based on pso, RRT (Rapidly-exploring random tree) and BLP (Bi-level programming) is compared in real-time mode to analyze the advantages and disadvantages of pso based path planning

关键词： UAV Path planning pso algorithm Logistic chaos model Dijkstra algorithm

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Joint inversion of TEM and DC in roadway advanced detection based on particle swarm optimization

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JOURNAL OF APPLIED GEOPHYSICS 2015年 123卷 30-35页

作者： Cheng, Jiulong Li, Fei Peng, Suping Sun, Xiaoyun Zheng, Jing Jia, Jizhe China Univ Min & Technol State Key Lab Coal Resources & Safe Min Beijing 100083 Peoples R China North China Inst Sci & Technol Key Lab Mine Disaster Prevent & Control Beijing 101601 Peoples R China Shijiazhuang Tiedao Univ Sch Elect & Elect Engn Shijiazhuang 050043 Peoples R China

Transient electromagnetic method (TEM)and direct current method (DC)are two key widely applied methods for practical roadway detection, but both have their limitations. To take the advantage of each method, a synchronous nonlinear joint inversion method is proposed based on TEM and DC by using particle swarm optimization (pso)algorithm. Firstly, a model with double low resistance anomaly and interference is constructed to test the performance of the method. Then the independent inversion and joint inversion are calculated by using the model built above. It is demonstrated that the joint inversion helped in improving the interpretation of the data to get better results. It is because that the suppression of interference and separation of the resistivity anomalies ahead and the back of the roadway working face using the proposed method. Finally, the proposed method was successfully used in a coalmine in Huainan coalfield in east China to demonstrate its practical usefulness. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Whole-space Advanced detection Transient electromagnetic method Direct current method Joint inversion pso algorithm

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Rapid synthesis of nanopowders in high energy ball milling;Optimization of milling parameters

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CERAMICS INTERNATIONAL 2015年第1期41卷 1631-1639页

作者： Abdellahi, Majid Bahmanpour, Marjan Islamic Azad Univ Najafabad Branch Dept Mat Engn Najafabad Iran Islamic Azad Univ Najafabad Branch Dept Comp Engn Najafabad Iran

An important factor in high energy ball milling process is to speed up the synthesis of metal/ceramic nanocomposites. In this work, for the first time, particle swarm optimization (pso) as a new powerful algorithm was used for optimization of milling parameters in order to minimize the synthesis time of metal/ceramic nanocomposites in planetary mills. Accordingly, it is important to find a mathematical model to correlate the milling parameters with the synthesis time of metal/ceramic nanocomposites and then optimize the mentioned model. In this work, Burgio's mathematical model was chosen because it describes the energy supplied by a planetary mill using only analytical expressions. Based on Burgio's model eight design parameters in milling, namely, number of balls, ball diameter, vial radius, vial height, ball diameter distribution coefficient, plate spinning rate, vial spinning rate and distance between the center of the plate and the center of the vial, were optimized by the pso algorithm. By optimizing the milling parameters and given the Burgio model, the synthesis time was minimized. At the end two test cases were solved to demonstrate the effectiveness and accuracy of the proposed design. Computational results showed that the proposed optimization algorithm is quite effective and powerful in optimizing the planetary mills to speed up the metal/ceramic nanocomposites synthesis. (C) 2014 Elsevier Ltd and Techna Group S.r.l. All rights reserved.

关键词： Milling Metal/ceramic nanocomposites Optimization Planetary mill pso algorithm

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Location effect on gold nano bi-domes based absorption coefficient

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OPTICAL AND QUANTUM ELECTRONICS 2015年第7期47卷 1713-1719页

作者： Akhlaghi, Majid Emami, Farzin Nozhat, Najmeh Islamic Azad Univ Young Researchers & Elite Club Omidieh Branch Omidieh Iran Shiraz Univ Technol Electro Opt Dept Shiraz Iran

Binary particle swarm optimization (Bpso) algorithm was used to generate the binary gold nano bi-domes for getting higher absorption coefficient spectrum. The simulation results show that absorption coefficient strongly depends on the localized position of nano particles and non-periodic structures have more appropriate response in term of absorption coefficient. This approach can be useful in optical applications such as solar cell and plasmonic nano antenna.

关键词： Plasmonic nano particles Plasmonic nano antenna Optimization algorithm pso algorithm

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