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International Conference on Computational Intelligence and Security

作者： Jinmao Xu Yaowen Xue Huimin Gao Pengzhu Zhang Division of System Simulation and Computer Application Taiyuan University of Science and Technology Taiyuan China College of Economics and Management Taiyuan University of Science and Technology Taiyuan China Division of System Simulation and Computer Application Taiyuan University of Science and Technology Taiyuan China Antai College of Economics and Management Shanghai Jiaotong university Shanghai China

ISBN: (纸本)9781424454112

During the study of abnormal capital flow, people can not access to a real financial transaction data as a result of many factors, therefore this article took the ways of intelligent agent role simulation, designed an agent decision-making model to verify the data collected. And from different perspectives the visualization of supervision to capital abnormal flow can be achieved.

关键词： Monitoring Computational modeling computer simulation Decision making Libraries computer applications Educational institutions Conference management Financial management Technology management

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Globally Asymptotically Stable for Exponential Type Stochastic Swarms

Globally Asymptotically Stable for Exponential Type Stochast...

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International Symposium on Information Science and Engineering, ISISE

作者： Zhibin Xue Jianchao Zeng College of Electric & Information Engineering Lanzhou University of Technology Lanzhou China Division of System Simulation and Computer Application Taiyuan University of Science and Technology Taiyuan China

ISBN: (纸本)9781424463251;9780769539911

A novel Lagrangian ¿individual-based¿ isotropic continuous time exponential type stochastic swarming model in an n-dimensional Euclidean space with a family of attraction/repulsion function is proposed in this article. The stability of aggregating behavior of the swarms system are verified by stability theoretical analysis and numerical simulation. Stability analysis and numerical simulations results further indicate that the individual members living in group during the course of coordinative motion can realize the mutual aggregating behavior, the motion of each individual member is a combination of the inter-individual interactions, meanwhile, which are also presented to demonstrate the effectiveness of our model. The attraction/repulsion function is odd, so the attractive force and repulsion force taking effect in opposite direction that leads to aggregation behavior. Moreover, numerical simulation results of globally asymptotically stability analysis also verify the capability of the proposed relevant theories.

关键词： Stochastic processes Biological system modeling Stability analysis Numerical simulation Educational institutions Marine animals Chemical technology Particle swarm optimization Intelligent robots Animal behavior

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A New Vector Particle Swarm Optimization for Constrained Optimization Problems

A New Vector Particle Swarm Optimization for Constrained Opt...

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International Joint Conference on Computational Sciences and Optimization, CSO

作者： Chao-li Sun Jian-chao Zeng Jeng-Shyang Pan Division of System Simulation and Computer Application Taiyuan University of Science and Technology Taiyuan China Department of Electronic Engineering National Kaohsiung University of Applied Sciences Kaohsiung Taiwan

A new vector particle swarm optimization (NVPSO) is proposed to solve constrained optimization problems in this paper. In NVPSO, we introduce a shrinkage coefficient to ensure that all dimensions of a particle are within lower and upper bounds, and a new function to determine whether the particle is within the feasible region. One-dimensional search optimization methods are selected in NVPSO algorithm to produce a new position which is guaranteed to be in the feasible region for the particle which escapes from the feasible region. The whole process is dealt as vector mode. The experimental results show that the principle of NVPSO this paper proposed is simple, relatively effective and efficient.

关键词： Particle swarm optimization Constraint optimization Upper bound Computational modeling computer simulation computer applications Optimization methods Chaos Sun Stochastic processes

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Practical Stability Analysis for Exponential Type Stochastic Swarms

Practical Stability Analysis for Exponential Type Stochastic...

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International Conference on Computational Intelligence and Software Engineering (CiSE)

作者： Zhibin Xue Jianchao Zeng Department of chemical machinery Chemical engineering College Qinghai University Xining China College of Electric & Information Engineering Lanzhou University of Technology Lanzhou China Division of System Simulation and Computer Application Taiyuan University of Technology Taiyuan China

A novel Lagrangian "individual-based" isotropic continuous time exponential type stochastic swarming model in an n-dimensional Euclidean space with a family of attraction/repulsion function is proposed in this article. The stability of aggregating behavior of the swarms system are verified by practical stability theoretical analysis and numerical simulation. Practical stability analysis and numerical simulations results further indicate that the individual members living in group during the course of coordinative motion can realize the mutual aggregating behavior, the motion of each individual member is a combination of the inter-individual interactions, meanwhile, which are also presented to demonstrate the effectiveness of our model. The attraction/repulsion function is odd, so the attractive force and repulsion force taking effect in opposite direction that leads to aggregation behavior.

关键词： Stability analysis Stochastic processes Biological system modeling Numerical simulation Educational institutions Marine animals Animal behavior Robot kinematics Chemical technology Particle swarm optimization

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A modified particle swarm optimization based on a Chinese archaism

A modified particle swarm optimization based on a Chinese ar...

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3rd International Conference on Innovative Computing Information and Control, ICICIC'08

作者： Chu, Yongfang Cui, Zhihua Zeng, Jianchao Liu, Jing Division of System Simulation and Computer Application Taiyuan University of Science and Technology 030024 China

ISBN: (纸本)9780769531618

Particle swarm optimization with passive congregation (PSOPC) was a new variant by adding an attraction of passive congregation. However, the performance of PSOPC is not stable when solving the multimodal benchmarks. To overcome this shortcoming, a modified particle swarm optimization based on a Chinese archaism (PSOCA) is designed. The experimental results demonstrate much better performance of the PSOCA in solving multimodal problems than that of the PSOPC and other two variants. © 2008 IEEE.

关键词： Particle swarm optimization (PSO)

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A hybrid intelligent algorithm for stochastic dependent-chance programming

A hybrid intelligent algorithm for stochastic dependent-chan...

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1st International Workshop on Knowledge Discovery and Data Mining, WKDD

作者： Ning, Xiao Jianchao, Zeng Division of System Simulation and Computer Application Taiyuan University of Science and Technology Shanxi China

ISBN: (纸本)0769530907

The stochastic dependent-chance programming belongs to a class of stochastic programming problems, which has wide application backgrounds, in order to search an algorithm which can more effectively solve this problem, in the paper, stochastic simulation is used to produce training samples for BP neural networks, and a hybrid intelligent algorithm for stochastic dependent-chance programming combined PSO algorithm with BP neural networks for approximation of the chance function is presented. Finally, the simulation results of two examples are given to show the correctness and effectiveness of the algorithm. © 2008 IEEE.

关键词： Stochastic programming

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Apical-dominant particle swarm optimization

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Progress in Natural Science:Materials International 2008年第12期18卷 1577-1582页

作者： Zhihua Cui~(a,b) Xingjuan Cai~b Jianchao Zeng~b Guoji Sun~a a State Key Laboratory for Manufacturing systems Engineering,Xi’an Jiaotong University,Xi’an 710049,China b division of system simulation and computer application,Taiyuan University of Science and Technology,Taiyuan 030024,China [a]State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong University Xi’an 710049 China [b]Division of System Simulation and Computer Application Taiyuan University of Science and Technology Taiyuan 030024 China

Particle swarm optimization (PSO) is a new stochastic population-based search methodology by simulating the animal social behaviors such as birds flocking and fish *** improvements have been proposed within the framework of this biological assumption. However,in this paper,the search pattern of PSO is used to model the branch growth process of natural *** provides a different poten- tial manner from artificial *** illustrate the effectiveness of this new model,apical dominance phenomenon is introduced to construct a ncvel variant by emphasizing the influence of the *** this improvement,the population is divided into three different kinds of buds associated with their ***,a mutation strategy is applied to enhance the ability escaping from a local ***- ulation results demonstrate good performance of the new method when solving high-dimensional multi-modal problems.

关键词： Apical-dominance phenomenon Particle swarm optimization Branch growth model High-dimensional multi-modal benchmarks

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A Modified Particle Swarm Optimization Based on a Chinese Archaism

A Modified Particle Swarm Optimization Based on a Chinese Ar...

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International Conference on Innovative Computing, Information and Control (ICICIC)

作者： Yongfang Chu Zhihua Cui Jianchao Zeng Jing Liu Division of System Simulation and Computer Application Taiyuan University of Science and Technology China

ISBN: (纸本)9781424431977;9780769531618

关键词： Particle swarm optimization Animals Convergence Equations Acceleration Benchmark testing Conferences

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Chaos-Mutation-Based Particle Swarm Optimizer for Dynamic Environment

Chaos-Mutation-Based Particle Swarm Optimizer for Dynamic En...

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2008 3rd International Conference on Intelligent system and Knowledge Engineering(第三届智能系统与知识工程国际会议)(ISKE 2008)

作者： Dianmin Dang Jing Jie Jianchao Zeng Meng Wang Division of System Simulation and Computer Application Taiyuan University of Science and TechnologyTaiyuan 030024China

The paper presents a modified Particle SwarmOptimization(PSO)for the dynamic *** method provides a detected position for eachparticle,and applies the detected positions of somerandomly sampled particles in the swarm to detect thedynamic change of the *** the environmenthas been detected to change,chaos mutationtechnology will be introduced to respond to the changein *** that,an improved chaos mutation guidedby swarm diversity has been developed to improve theresponding *** proposed method has beenapplied to the dynamic environment constructed by theparabola *** simulation results show theimproved PSO can detect changes more accurately andrespond to the changes of the environment morequickly,and has been a robust technique for thedynamic optimization.

关键词： Chaos Particle swarm optimization Genetic mutations Intelligent systems Knowledge engineering Birds Computational modeling computer simulation computer applications Paper technology

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An Improved PSO with Time-Varying Accelerator Coefficients

An Improved PSO with Time-Varying Accelerator Coefficients

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International Conference on Intelligent systems Design and applications (ISDA)

作者： Zhihua Cui Jianchao Zeng Yufeng Yin Division of System Simulation and Computer Application Taiyuan University of Science and Technology Taiyuan Shanxi China

Cognitive and social learning factors are two important parameters of particle swarm optimization (PSO), and many different settings have been proposed, in which one famous strategy is the linear manner proposed by Ratnaweera. However, due to the complex nature of the optimization problems, linear-type setting may not work well in many cases. Since the large cognitive coefficient provides a large local search capability, as well as the small one employs a large global search capability, three different non-linear settings are designed to further investigate the potential advantages among these two parameters. simulation results show the concave function strategy is an effective manner especially for multi-modal functions.

关键词： Particle swarm optimization Particle accelerators Linear accelerators Computational modeling Intelligent systems application software Time varying systems computer simulation computer applications Statistical analysis

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