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

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Network Intrusion Detection Analysis with Neural Network and particle swarm optimization algorithm

Network Intrusion Detection Analysis with Neural Network and...

引用

22nd Chinese Control and Decision Conference

作者： Tian, WenJie Liu, JiCheng Beijing Union Univ Beijing Automat Inst Beijing 100101 Peoples R China

ISBN: (纸本)9781424451821

Because the network intrusion behaviors are characterized with uncertainty, complexity and diversity, an intrusion detection method based on neural network and particle swarm optimization algorithm (PSOA) is presented in this paper. The novel structure model has higher accuracy and faster convergence speed. We construct the network structure, and give the algorithm flow. We discussed and analyzed the impact factor of intrusion behaviors. With the ability of strong self-learning and faster convergence, this intrusion detection method can detect various intrusion behaviors rapidly and effectively by learning the typical intrusion characteristic information. Utilizing the character that rough set can keep the discern ability of original dataset after reduction, the reduces of the original dataset arc calculated and used to train neural network, which increase the detection accuracy. We apply this technique on KDD99 data set and get satisfactory results. The experimental result shows that this intrusion detection method is feasible and effective.

关键词： Neural Network Rough Set Network Intrusion Reduction particle swarm optimization algorithm

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A new algorithm based on the Gbest of particle swarm optimization algorithm to improve Estimation of Distribution algorithm

A new Algorithm based on the Gbest of Particle Swarm Optimiz...

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International Conference on Smart Computing and Electronic Enterprise (ICSCEE)

作者： Zhao, Qiuyue Gao, Ying Guangzhou Univ Comp Sci & Educ Software Inst Guangzhou Peoples R China

ISBN: (纸本)9781538648384

In recent years, with the rise of artificial intelligence and deep learning, as an evolutionary algorithm based on probability model, estimation of distribution algorithm has been widely research and development. The estimation of distribution algorithm without the traditional genetic operation such as crossover and mutation, is a new kind of evolution model. As an algorithm based on probabilistic mode, the estimation of distribution algorithm establishes a probabilistic model describing the solution space of optimization problems. With the emergence for big data, the convergence of the algorithm and the requirements for solving precision are also increasing. This paper attempts to improve the distribution estimation algorithm. The optimal population of each iteration is found through the location update of each iteration of the particle swarm optimization (PSO) algorithm. The simulation test was carried out with ten benchmark test function. The proposed algorithm was compared with the GA_EDA9improved genetic algorithm) and the basic distribution estimation (EDA) algorithm. Experimental results show that the new algorithm is superior to GA_EDA and basic EDA in terms of convergence and accuracy.

关键词： Estimation of distribution algorithm particle swarm optimization algorithm simulation

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Solving traveling salesman problem based on improved particle swarm optimization algorithm

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11th Joint International Computer Conference (JICC 2005)

作者： Wang, CR Zhang, JW Yang, J Sun, CJ Feng, HX Yuan, HJ N China Elect Power Univ Sch Comp Sci & Technol Baoding 071003 Peoples R China

ISBN: (纸本)9812565329

An improved particle swarm optimization (IPSO) algorithm is proposed to solve a typical combinatorial optimization problem: traveling salesman problem, which is a well-known NP-complete problem. In the improved algorithm, particles not only adjust its own flying speed according to itself and the best individual of the swarm but also learn from other individuals according to certain probability. This kind of study behavior accords with the biological natural law even more, and furthermore helps to find the global optimum solution. At the same time, this paper proposes the concepts of Adjustment Operator and Adjustment Sequence based on which particle swarm optimization (PSO) and IPSO algorithm were successfully rebuilt, according to the ideas of single node regulating algorithm. For solving traveling salesman problem, numerical simulation results show the effectiveness and efficiency of the proposed method.

关键词： particle swarm optimization algorithm traveling salesman problem combinatorial optimization

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Application of Discrete particle swarm optimization algorithm to Aviation Rescue Base Location

Application of Discrete Particle Swarm Optimization Algorith...

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Chinese Intelligent Automation Conference (CIAC)

作者： Wu, Xiuyu Xing, Guoping Chen, Yongren Wu, Lijie Aviat Univ Air Force PLA Dept Aviat Lifesaving 7855 Renmin St Changchun 130022 Peoples R China Aviat Univ Air Force PLA Changchun 130022 Peoples R China Chengde Petr Coll Dept Foreign Language & Traveling Chengde 067000 Peoples R China

ISBN: (纸本)9783642385247;9783642385230

Based on the theoretic study of location of general facilities, this paper makes an attempt to optimize the typical discrete element of the location of aviation rescue base through discrete binary particle swarm (PSO) algorithm in order to find out an optimized location method with more simplified calculation and more optimized result, which will finally provide a solid theoretical foundation for the location of aviation rescue base.

关键词： Aviation rescue base Location model particle swarm optimization algorithm

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particle swarm optimization algorithm with compression factor is used to solve complex shading MPPT problem

Particle swarm optimization algorithm with compression facto...

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作者： Yanping Wang Zhen Zhang School of Electrical Engineering Zhengzhou University

In photovoltaic power generation systems, pv arrays are often affected by local shadow phenomena, resulting in the unstable operation of the system and the reduction of output power. In addition, pv array's p-u characteristic curve will show multiple peaks, and the traditional maximum power point tracking(MPPT) algorithm cannot complete the tracking of the maximum power point because it can only find the single peak. particle swarm optimization(PSO) algorithm has good global optimization ability of multi-peak, which is widely used in tracking the maximum power point of local shadow. However, PSO algorithm has the shortcoming of insufficient convergence speed and low search accuracy. Therefore, a particle swarm optimization(YSPSO) algorithm with compression factor is proposed to effectively improve the global search ability and local improvement ability of the whole algorithm.

关键词： MPPT problem particle swarm optimization algorithm compression factor

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Research on Minimum Copper Loss Control Strategy Based on particle swarm optimization algorithm 4th

Research on Minimum Copper Loss Control Strategy Based on Pa...

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4th International Symposium on New Energy and Electrical Technology (ISNEET)

作者： Zhou, Xuan Wang, Xiaona Naval Univ Engn Sch Elect Engn Wuhan 430033 Hubei Peoples R China

ISBN: (纸本)9789819770496;9789819770472;9789819770465

The open winding permanent magnet synchronous motor is driven by two sets of inverters and has the characteristics of high power and torque, which is applied to electric vehicles. This article aims to reduce the copper loss of open winding permanent magnet synchronous motors and proposes a variable decoupling angle modulation strategy based on the system. This strategy takes the angle (decoupling angle) between the output voltage vectors of two sets of inverters as the control variable, and uses particle swarm optimization algorithm to select the optimal decoupling angle based on different load states of the motor, driving an open-winding permanent magnet synchronous motor to reduce copper consumption. Through simulation verification, this strategy effectively reduces motor copper consumption and provides theoretical support for controlling electric vehicle operating losses.

关键词： Open-winding permanent magnet synchronous motor particle swarm optimization algorithm copper Loss

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A particle swarm optimization algorithm based on Orthogonal Design

A Particle Swarm Optimization Algorithm based on Orthogonal ...

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2010 IEEE World Congress on Computational Intelligence

作者： Yang, Jie Bouzerdoum, Abdesselam Phung, Son Lam Univ Wollongong Sch Elect Comp & Telecommun Engn Wollongong NSW 2522 Australia

ISBN: (纸本)9781424481262

The last decade has witnessed a great interest in using evolutionary algorithms, such as genetic algorithms, evolutionary strategies and particle swarm optimization (PSO), for multivariate optimization. This paper presents a hybrid algorithm for searching a complex domain space, by combining the PSO and orthogonal design. In the standard PSO, each particle focuses only on the error propagated back from the best particle, without "communicating" with other particles. In our approach, this limitation of the standard PSO is overcome by using a novel crossover operator based on orthogonal design. Furthermore, instead of the "generating-and-updating" model in the standard PSO, the elitism preservation strategy is applied to determine the possible movements of the candidate particles in the subsequent iterations. Experimental results demonstrate that our algorithm has a better performance compared to existing methods, including five PSO algorithms and three evolutionary algorithms.

关键词： algorithm theory elitism preservation strategy evolutionary algorithm evolutionary computation evolutionary strategy generating-and-updating model genetic algorithm multivariate optimization orthogonal design particle swarm optimisation particle swarm optimization algorithm

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Determine the optimal switching angles symmetrical cascaded multilevel inverter using Improved particle swarm optimization algorithm 18

Determine the optimal switching angles symmetrical cascaded ...

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18th IEEE World Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Ahmadi, Sajjad Sahnesaraie, Mostafa Andalib Talami, Saeid Hojjati Dini, Fakhteh Zanganeh, Mahnaz Mohebbi Ashgevari, Yazdan Islamic Azad Univ Dept Elect Engn Ardabil Sci & Res Branch Ardebil Iran Alborz Elect Power Distribut Alborz Iran Islamic Azad Univ Dept Elect Engn Mehriz Branch Mehriz Iran Shahid Beheshti Univ Fac Elect & Comp Engn Tehran Iran Islamic Azad Univ Dept Elect Engn Ardabil Branch Ardebil Iran

ISBN: (纸本)9781728131498

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 multi-level 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

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Intrusion Detection Quantitative Analysis with Support Vector Regression and particle swarm optimization algorithm

Intrusion Detection Quantitative Analysis with Support Vecto...

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International Conference on Wireless Networks and Information Systems

作者： Tian, WenJie Liu, JiCheng Beijing Union Univ Automat Inst Beijing Peoples R China

ISBN: (纸本)9780769539010

Because the network intrusion behaviors are characterized with uncertainty, complexity and diversity, a new method based on support vector regression (SVR) and particle swarm optimization algorithm (PSOA) is presented and used for pattern analysis of intrusion detection in this paper. The novel structure model has higher accuracy and faster convergence speed. We construct the network structure, and give the algorithm flow. We discussed and analyzed the impact factor of intrusion behaviors. With the ability of strong self-learning and faster convergence, this intrusion detection method can detect various intrusion behaviors rapidly and effectively by learning the typical intrusion characteristic information. We use rough set to reduce dimension. We apply this technique on KDD99 data set and get satisfactory results. The experimental result shows that this intrusion detection method is feasible and effective.

关键词： support vector regression intrusion detection particle swarm optimization algorithm rough set convergence

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A particle swarm optimization algorithm and its application in hydrodynamic equations

A particle swarm optimization algorithm and its application ...

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2nd International Conference on Machinery, Materials Science and Engineering Applications (MMSE 2012)

作者： Zhou, Jianhui Zhao, Shuzhong Yue, Lixi Lu, Yannan Si, Xinyi Hebei United Univ Coll Qianan Tangshan Peoples R China Hehai Univ Coll Water Conservancy & Hydropower Engn Nanjing Peoples R China

ISBN: (纸本)9783037854099

In fluid mechanics, how to solve multiple solutions in ordinary differential equations is always a concerned and difficult problem. A particle swarm optimization algorithm combining with the direct search method (DSPO) is proposed for solving the parameter estimation problems of the multiple solutions in fluid mechanics. This algorithm has improved greatly in precision and the success rate. In this paper, multiple solutions can be found through changing accuracy and search coverage and multi-iterations of computer. Parameter estimation problems of the multiple solutions of ordinary differential equations are calculated, and the result has great accuracy and this method is practical.

关键词： particle swarm optimization algorithm Direct search method Ordinary differential equations Parameter estimation

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