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International Conference on Computational Aspects of Social Networks

作者： Liu, Jinxia Zeng, Jianchao College of Electrical and 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)9780769542027

Detecting and characterizing the community structure of complex network and social network is fundamental problem. Many of the proposed algorithm for detecting community based on modularity Q which fail to identify modules smaller than a scale community. In this paper, authors propose a new community detection algorithm based on genetic algorithm and modularity density (D value). We test our method on classical social networks whose community structure is already known and the results can be much easier compared with the method. Experiments show the capability of the method to successfully detect the community structure. © 2010 IEEE.

关键词： Genetic algorithms

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Particle swarm optimization with non-linear individual cognitive strategy

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Journal of Information and Computational Science 2009年第1期6卷 123-129页

作者： Cui, Zhihua Cai, Xingjuan Zeng, Jianchao Division of System Simulation and Computer Application Taiyuan University of Science and Technology Taiyuan 030024 China

Cognitive learning factor is an important parameter used to control the weight of current position. However, it is always setting the same value for each particle in previous literatures. This type of setting ignores the different performances among particles' current positions. Generally, the better performance of one position means there is a more probability that global optima falls around it. This phenomenon implies the selection strategy of cognitive coefficient with individual setting, instead of uniform control, may provide better result. Thus, a modified version of particle swarm optimization with non-linear individual cognitive selection strategy is designed, in which the cognitive learning factor of each particle is determined according to the different current position's performance. Furthermore, a new mutation strategy is introduced to avoid premature convergence. simulation results show the proposed method is effective and efficient. © 2009 Binary Information Press.

关键词： Particle swarm optimization (PSO)

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Self-learning particle swarm optimization based on environmental feedback

Self-learning particle swarm optimization based on environme...

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2nd International Conference on Innovative Computing, Information and Control, ICICIC 2007

作者： Cai, Xingjuan Cui, Zhihua Zeng, Jianchao Tan, Ying Division of System Simulation and Computer Application Taiyuan University of Science and Technology Shanxi 030024 China State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong University Xi'an 710049 China

ISBN: (纸本)0769528821

Particle swarm optimization (PSO) simulates the behaviors of birds ocking and £sh schooling. However, its biological background does not concern the environmental affection. Inspired by the interaction between environment and individuals, a new version - self-learning particle swarm optimization based on environmental feedback (SL-PSO), is proposed, in which two self-learning strategies are designed so that each particle adjusts its moving direction according to the feedback information from the environment. Furthermore, a mutation operator is introduced to avoid premature convergence phenomenon. simulation results show the proposed algorithm is effective and ef£cient. © 2007 IEEE.

关键词： Particle swarm optimization (PSO)

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Studying of nonlinear genetic algorithm

Studying of nonlinear genetic algorithm

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World Congress on Intelligent Control and Automation (WCICA)

作者： Zhihua Cui Jianchao Zeng Division of system simulation and computer application Taiyuan Heavy Machinery Institute China

Through mechanism analysis of simple GA (SGA), every genetic operator of SGA is found be equal to linear transformation of chosen individual. So the linear transformation is changed for the improvement of the algorithm capability. As a result, a new genetic algorithm-nonlinear GA (NGA) is conducted. The optimization computing of some examples is made to show that the new genetic algorithm has better global search capacity and rapid convergence rate.

关键词： Genetic algorithms Computational modeling Analytical models computer simulation computer applications Machinery Convergence

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Stochastic Petri nets model of hybrid dynamical system

Stochastic Petri nets model of hybrid dynamical system

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World Congress on Intelligent Control and Automation (WCICA)

作者： Li Zhongwei Zeng Jianchao Division of system simulation and computer application Taiyuan Heavy Machinery Institute China

Based on the theory of hybrid dynamical system and stochastic Petri nets, stochastic Petri nets model of hybrid dynamical system is presented in this paper. The qualitative change and quantitative change rules of the model are put forward, and state tracking of hybrid states is studied in the paper. This stochastic Petri nets applies to modeling and analysis of hybrid dynamical system with random factors effectively.

关键词： Stochastic systems Petri nets Computational modeling computer simulation computer applications Machinery Intelligent control Automation

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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

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.

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

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Forecasted Particle Swarm Optimization

Forecasted Particle Swarm Optimization

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International Conference on Natural Computation (ICNC)

作者： Xingjuan Cai Jianchao Zeng Ying Tan Division of System Simulation and Computer Application Taiyuan University of Science and Technology China

This paper introduces a novel fitness estimation strategy for particle swarm optimization (PSO) that does not evaluate all new positions, thus operating faster. A fitness and associated reliability value are assigned to each new individual that is only evaluated using the true fitness function if the reliability value is below some threshold. This variant of PSO designs a two-stage convex fitness estimation method. The first stage is used to estimate a visual position's fitness and reliability value, whereas in the second stage, the individual's fitness and reliability value are estimated with this visual position. simulation results show the proposed algorithm is effective and efficient.

关键词： Particle swarm optimization Computational modeling Equations Technology forecasting Predictive models computer simulation computer applications Birds Random number generation Evolutionary computation

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Mind evolutionary computation for the constrained optimization

Mind evolutionary computation for the constrained optimizati...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jing Jie Jian-Chao Zeng Su-Lan Zhang Division of System Simulation and Computer Application Taipan Heavy Machinery Institute Taiyuan China

In this paper, the mind evolutionary computation (MEC) was applied to solve the constrained optimization problems. First, several concepts are presented and a valid method to handle the constraints is developed based on those concepts. After that, according to the features of the constrained problems, the 'similartaxis' and dissimilation operators in MEC are redesigned in details. Finally, the simulation results for the nonlinear constrained problem show the effectiveness of the algorithm in solving the constrained problems.

关键词： Evolutionary computation Constraint optimization Computational modeling computer simulation computer applications Machinery Algorithm design and analysis Genetic algorithms Scattering Gaussian distribution

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Mind evolutionary computation with swarm intelligence

Mind evolutionary computation with swarm intelligence

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Gang-Zhu Qiao Jing Jie Jian-Chao Zeng Xiao-Juan He Division of System Simulation and Computer Application Taipan Heavy Machinery Institute Taiyuan China

ISBN: (纸本)0780384032

Mind evolutionary computation (MEC) is a novel stochastic algorithm that derived from man's swarm intelligence. Based on the swarm theory, the social behavior analysis about MEC is made and the searching mechanisms of its operators are studied. After that, a parameter analysis is provided for the similar axis operator, and a cooperation-based dissimilation operator (CDO) is developed. Finally, a series of experiments have been done to make a parameter choice and an evaluation for MEC. The results illustrate MEC with CDO is a viable global optimization method owning robust ability.

关键词： Evolutionary computation Particle swarm optimization Computational modeling Optimization methods Biological system modeling Evolution (biology) Analytical models Algorithm design and analysis Robustness Biology computing

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A two-order Particle Swarm Optimization Model and the Selection of its Parameters

A two-order Particle Swarm Optimization Model and the Select...

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World Congress on Intelligent Control and Automation (WCICA)

作者： Jianxiu Hu Jianchao Zeng Yaping Yang Division of system simulation & computer application Taiyuan University of Science and Technology Taiyuan China

Based on the analysis of theoretical model and evolutionary equations for the standard PSO, a new particle swarm optimization model, called the two-order PSO, was performed, which simulated more precisely the velocity change of particles than the standard PSO. At the same time a new method termed pso for pso was used for the selection of the best parameters c1, c2 in the two-order PSO. The results on five benchmark functions prove the feasibility and validity of this method. Furthermore, the analysis of the results shows that there are revelatory rules on selection of c1, c2 in the two-order PSO

关键词： Particle swarm optimization Computational modeling Analytical models computer simulation computer applications Electronic mail Performance analysis Equations Intelligent control Automation

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