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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 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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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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Virtual network mapping algorithm based on load balancing multi-objective particle swarm optimization

Virtual network mapping algorithm based on load balancing mu...

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11th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM 2015)

作者： Jun Zhao Zhong Ma Wuhan Digital Engineering Institute China

ISBN: (纸本)9781510821279

Existing virtual network mapping algorithms does not consider resource consumption of intermediate node on communication path usually. Minimum resource consumption or shortest path of physical network is regarded as objective, thereby leading to bottleneck due to insufficient resource of intermediate node on communication path, and affecting performance of the whole physical network and subsequent success rate of virtual network. A virtual network mapping algorithm based on load balancing multi-objective particle swarm optimization is proposed in the paper aiming at the problem. Resource consumption of intermediate node is sufficiently considered in the algorithm, double balance of node load and link load is regarded as objective. Meanwhile, the optimal path of particle swarm optimization algorithm is adopted. Experiments show that the algorithm proposed in the paper can not only realize double balance of node load and link load, but also effectively improve request receiving success rate, overall resource load balance and long-term operation income.

关键词： Load Balancing particle swarm optimization algorithm Virtual Network Mapping Load balancing intermediate node Link Load Telecommunication lines particle swarm optimization Virtual networks particle swarm algorithm Resource utilization physical network

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particle swarm optimization algorithm Improvement and Application

Particle Swarm Optimization Algorithm Improvement and Applic...

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2010 3rd IEEE International Conference on Computer Science and Information Technology—ICCSIT 2010

作者： Xiaoli Baojunjie and Kuanghang Department of Computer Science,Chongqing Education College Chongqing,400067,China

in this paper,through the research of the existing particle swarm optimization algorithm and its improved algorithm,a particle swarm optimization algorithm improvement program is proposed,and the experimental results show that this improved algorithm not only does not increase the complexity,but also has greater improvement in the convergence speed and stability comparing with the original algorithm.

关键词： particle swarm optimization algorithm dynamic weight aggregation population diversity convergence speed

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An Acoustic Echo Cancellation Scheme Based on particle swarm optimization algorithm

An Acoustic Echo Cancellation Scheme Based on Particle Swarm...

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IEEE Region 10 Conference on TENCON 2010

作者： Mahbub, Upal Acharjee, Partha Pratim Fattah, Shaikh Anowarul Bangladesh Univ Engn & Technol Dept Elect & Elect Engn Dhaka 1000 Bangladesh

ISBN: (纸本)9781424468904

This paper deals with the problem of echo cancellation of speech signals in an acoustic environment. In this regard, generally, different adaptive filter algorithms are employed, which may lack the flexibility of controlling the convergence rate, number of iterations, range of variation of filter coefficients, and tolerance consistency. In order to overcome these problems, unlike conventional approaches, we formulate the task of echo cancelation as a coefficient optimization problem whereby we introduce the particle swarm optimization (PSO) algorithm. In this case, the PSO is designed to perform the error minimization in frequency domain. From extensive experimentations, it is shown that the proposed PSO based acoustic echo cancellation method provides high echo cancellation performance in terms of echo return loss enhancement with a faster convergence rate in comparison to that obtained by some of the state-of-the-art methods.

关键词： Echo cancellation adaptive filter particle swarm optimization algorithm speech analysis frequency domain optimization

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Economic Dispatch Considering Ancillary Service Based on Revised particle swarm optimization algorithm

Economic Dispatch Considering Ancillary Service Based on Rev...

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6th International Conference on Intelligent Computing

作者： Ma, Xin Liu, Yong North China Univ Water Conservancy & Elect Power Sch Management & Econ Zhengzhou 450011 Henan Province Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China

ISBN: (纸本)9783642149214

A revised strategy particle swarm optimization algorithm is proposed to solve the economic dispatch problems in power systems Many constraints such as ramp rate limits and prohibited zones are taken into account and the loss is also calculated On the basis of strategy particle swarm optimization algorithm a new revised strategy is provided to handle the constraints and make sure the particles to satisfy the constraints The strategy can guarantee the particles to search in or around the feasible solutions area combined with penalty functions The accuracy and speed of the algorithm are improved for the particles will rarely search in the infeasible solutions area and the results also show that the new algorithm has a fast speed high accuracy and good convergence

关键词： Electricity market economic dispatch ancillary service particle swarm optimization algorithm revise strategy

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Grey Model Based particle swarm optimization algorithm for Anticorrosion Reliability Design of Underground Pipelines

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2nd International Conference on Advances in Product Development and Reliability (PDR)

作者： Liu, Qinming Dong, Ming Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Coll Econ & Management Shanghai 200052 Peoples R China

ISBN: (纸本)9780878492541

This paper explores the grey model based PSO (particle swarm optimization) algorithm for anti-cauterization reliability design of underground pipelines. First, depending on underground pipelines' corrosion status, failure modes such as leakage and breakage are studied. Then, a grey GM(1,1) model based PSO algorithm is employed to the reliability design of the pipelines. One important advantage of the proposed algorithm is that only fewer data is used for reliability design. Finally, applications are used to illustrate the effectiveness and efficiency of the proposed approach.

关键词： Underground pipeline Reliability design Grey theory particle swarm optimization algorithm

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Utilization Efficiency Forecasting of Moisture Content in Maize Based on particle swarm optimization algorithm and RBF Neural Network

Utilization Efficiency Forecasting of Moisture Content in Ma...

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2nd International Conference on Computer and Automation Engineering (ICCAE)

作者： Yu Xiaofang Gao Jvlin Song Guodong Inner Mongol Agr Univ Hohhot 010010 Peoples R China Inner Mongolia Autonomous Reg Seed Control Stn Hohhot 010010 Peoples R China

ISBN: (纸本)9781424455690

Utilization efficiency forecasting of moisture content in maize has a great importance to maize production. RBF neural network is able to universal approximation. PSO-RBF neural network which combines particle swarm optimization (PSO) with RBF neural network is proposed to utilization efficiency forecasting of moisture content in maize. Maize fields of the farms in Henan province are applied to study the utilization efficiency forecasting ability of moisture content in maize by the proposed PSO-RBF neural network method. And BP neural network and normal RBF neural network are applied to compare the PSO-RBF neural network method. By analyzing the experimental results, it is indicated that utilization efficiency forecasting ability of moisture content in maize by PSO-RBF neural network than that by RBF neural network and BP neural network.

关键词： utilization efficiency moisture content RBF neural network particle swarm optimization algorithm

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Integration of particle swarm optimization and Immune Genetic algorithm-Based Dynamic Clustering for Customer Clustering

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2015年第5期24卷 1550019-1550019页

作者： Kuo, R. J. Lin, S. H. Chen, Zhen-Yao Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei 10607 Taiwan Taiwan Semicond Mfg Co Hsinchu 30078 Taiwan De Lin Inst Technol Dept Business Adm New Taipei City 23654 Taiwan

This study intends to present a dynamic clustering (DC) approach based on particle swarm optimization (PSO) and immune genetic (IG) (DCPIG) algorithm, which is able to cluster the data into adequate clusters through data characteristics with pre-specified numbers of clusters. The proposed DCPIG algorithm is compared with three DC algorithms in the literature using Iris, Wine, Glass and Vowel benchmark data sets. The experiment results show that the DCPIG algorithm can achieve higher stability and accuracy than the other algorithms. In addition, the DCPIG algorithm is also applied to a real-world problem considering the customer clustering for a cyber flower shop. Lastly, we recommend different products and services to customers based on the clustering results.

关键词： Cluster analysis dynamic clustering particle swarm optimization algorithm immune genetic algorithm

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