检索结果-内蒙古大学图书馆

Research of Asynchronous Communication Messaging Model Based on Time Decoupling in Distributed Object-Oriented Middleware

Research of Asynchronous Communication Messaging Model Based...

引用

ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD)

作者： Yanhong Zhao Yinzhang Guo Shangwang Bai Division of System Simulation & Computer Application Taiyuan University of Science and Technology Taiyuan Shaanxi China

To solve the problem that the current distributed object-oriented middleware messaging communication mechanism doesn't support time decoupling and service quality control, a new asynchronous communication model TIAIM (time independent asynchronous invoke model) which can support asynchronous messaging and time decoupling is presented, we adopted message conversion layer in client to pick up, package and convert the asynchronous message, and convert original request to a routed message, and we designed a software route agent to achieve asynchronous message transmission and retransmission. TIAIM model supports asynchronous messaging, time decoupling and service quality effectively. This paper also provides algorithm of message conversion layer and software route agent.

关键词： Asynchronous communication Object oriented modeling Middleware application software Distributed computing Software quality Quality of service Computational modeling Packaging Information systems

来源：评论

学校读者我要写书评

暂无评论

Particle swarm optimization using Leávy probability distribution

引用

2nd International Symposium on Intelligence Computation and applications, ISICA 2007

作者： Cai, Xingjuan Zeng, Jianchao Cui, Zhihua 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)9783540745808

Velocity threshold is an important parameter to affect the performance of particle swarm optimization. In this paper, a novel velocity threshold automation strategy is proposed by incorporated with Leávy probability distribution. Different from Gaussian and Cauchy distribution, it has an infinite second moment and is likely to generate an offspring that is far away from its parent. Therefore, this method employs a larger capability of the global exploration by providing a large velocity scale for each particle. simulation results show the proposed strategy is effective and efficient. © Springer-Verlag Berlin Heidelberg 2007.

关键词： Optimal systems

来源：评论

学校读者我要写书评

暂无评论

Hybrid Heuristics Scheduling Algorithm for Hot Strip Mill

Hybrid Heuristics Scheduling Algorithm for Hot Strip Mill

引用

International Conference on Natural Computation (ICNC)

作者： Wang Xiaoping Gao Huimin Zeng Jianchao School of Economics and Management Taiyuan University of Science and Technology China Division of System Simulation and Computer Application Taiyuan University of Science and Technology Taiyuan China

This paper introduces the scheduling algorithm of a computer simulation and scheduling system for hot strip mill. The mathematical model of hot strip mill scheduling problem (HSMSP) is formulated. A hybrid heuristics method is proposed, based on parallel strategy, which integrates domain expertise, genetic algorithm (GA) and tabu search (TS). Domain expertise is used to classify and sort production orders to generate initial solutions in parallel. GA is used to improve the initial solutions. And TS attempt to accelerate the convergence of the solutions by adopting swaps of 'single slab' and 'slab section'. 'Single slab' and 'slab section', which we firstly introduced, are new adjacent solutions (neighborhood). simulation results have shown that the proposed method is feasible and efficient.

关键词： Scheduling algorithm Strips Milling machines Slabs Processor scheduling computer simulation Mathematical model Genetic algorithms Production Acceleration

来源：评论

学校读者我要写书评

暂无评论

Self-adjusting predicted particle swarm optimization

引用

Journal of Information and Computational Science 2007年第2期4卷 483-496页

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

Predicted particle swarm optimization is an enhanced version aiming to increase the utilization ratio of velocity information, and the performance heavily relies upon the parameters settings. According to control theory, the trajectories of position and velocity vectors of each particle can be both viewed as oscillatory links, and the relationship between inertia weight and accelerator coefficients is obtained. Thus, a self-adjusting parameter strategy is proposed. simulation results show the new proposed strategy is powerful and useful.

关键词： Numerical methods

来源：评论

学校读者我要写书评

暂无评论

Self-learning Particle Swarm Optimization Based on Environmental Feedback

Self-learning Particle Swarm Optimization Based on Environme...

引用

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

作者： Xingjuan Cai Zhihua Cui Jianchao Zeng Ying Tan Division of system simulation and computer application Taiyuan University of Science and Technology Shanxi China State Key Laboratory for Manufacturing Systems Engineering Xi''an Jiaotong University Xi'an China

Particle swarm optimization (PSO) simulates the behaviors of birds ocking and poundsh 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 efpoundcient.

关键词： Particle swarm optimization Convergence Birds Biological system modeling Computational modeling computer simulation Educational institutions Genetic mutations State feedback computer applications

来源：评论

学校读者我要写书评

暂无评论

Two-order particle swarm optimization model

引用

Jisuanji Yanjiu yu Fazhan/computer Research and Development 2007年第11期44卷 1825-1831页

作者： Hu, Jianxiu Zeng, Jianchao Division of System Simulation and Computer Application Taiyuan University of Science and Technology Taiyuan 030024 China

Particle swarm optimization (PSO) is an evolutionary computation technique developed by Kennedy and Eberhart in 1995. The underlying motivation for the development of PSO algorithm is social behavior of animals such as bird flocking, fish schooling, and swarm theory. Now PSO has been proved to be very effective for some problems. However, like other stochastic algorithms, PSO also suffers from the premature convergence problem, especially in the large scale and complex problems. In order to improve the global convergent ability of the standard particle swarm optimization (SPSO), a new version of particle swarm optimization named by a two-order PSO model is developed. Firstly, a two-order PSO model is introduced, its convergence analysis is given, and at the same time its parameter choices are studied. Secondly, a PSO model with the stochastic inertia weight is educed from the evolutionary equations of the two-order PSO. Thirdly, the two-order oscillating PSO with an oscillating factor is provided to adjust the influence of the acceleration on the velocity, which can guarantee the two-order PSO to converge to the global optimization validly. Finally, the above-proposed models are used to some benchmark optimizations. The experimental results show the proposed models can overcome the premature problem validly, and outperform the standard PSO in the global search ability and convergent speed.

关键词： Global optimization

来源：评论

学校读者我要写书评

暂无评论

Predictive,-velocity modified particle Swarm optimization

Predictive,-velocity modified particle Swarm optimization

引用

26th Chinese Control Conference, CCC 2007

作者： Zhihua, Cui Xingjuan, Cai Jianchao, Zeng Guoji, Sun Division of System Simulation and Computer Application Taiyuan University of Science and Technology Taiyuan 0300240 China State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong University Xi'an 710049 China

ISBN: (纸本)7900719229

Velocity update equation of standard particle swarm optimization uses only the size information of previous velocity vector whereas ignoring the corresponding direction information. By adding predictive velocity, a new predictive-velocity modified particle swarm optimization (PVPSO) which using the size and direction information of velocity vectors to estimate and correct particle's next generation positions is proposed. Theoretical analysis proofs the new algorithm owns an enhanced global search capability. The optimization computing of some examples is made to show that the PVPSO has better global search capacity and rapid convergence speed.

关键词： Optimization

来源：评论

学校读者我要写书评

暂无评论

Self-adaptive PID-controlled particle swarm optimization

Self-adaptive PID-controlled particle swarm optimization

引用

26th Chinese Control Conference, CCC 2007

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

ISBN: (纸本)7900719229

As a new version of particle swarm optimization (PSO), PID-controlled PSO introduces the concept of controller into the algorithm structure. However, with the introduction of PID controller, three additional parameters are incorporated into the algorithm. Thus, how to provide a proper selection of these parameters is an important problem to affect the algorithm efficiency. In this paper, the relationships among these parameters are conducted by the stability theory. Further, a self-adaptive parameter selection strategy is proposed. simulation results show the proposed strategy is effective.

关键词： Self adjusting control systems

来源：评论

学校读者我要写书评

暂无评论

Adaptive Particle Swarm Optimization with PD Controller

Adaptive Particle Swarm Optimization with PD Controller

引用

Congress on Evolutionary Computation

作者： Jing Jie Jianchao Zeng Chongzhao Han Youzhi Ren Division of System Simulation & Computer Application Taiyuan University of Science and Technology Taiyuan China School of Electronics and Information Engineering Jiao Tong University Xi'an China China United Telecommunications Corporation China

The paper develops an adaptive particle swarm optimization(PSO) based on its simplification. With the hope to prompt its global optimization performance, the improved menthod introduces a PD controller into the architecture of the standard PSO(SPSO-PD). The PD controller can control the particle dynamics, and prompt the particles to respond to the change of their exemplars correctly and rapidly, further to surpass the limit of their exemplars with more chances, which greatly contributes to a successful global search. The proposed SPSO-PD was applied to some well-known benchmarks and compared with the standard PSO. The relative experimental results show SPSO-PD performs better than SPSO on the complex optimization functions.

关键词： Programmable control Adaptive control Particle swarm optimization PD control Evolutionary computation

来源：评论

学校读者我要写书评

暂无评论

An enhanced hybrid quadratic particle swarm optimization

An enhanced hybrid quadratic particle swarm optimization

引用

ISDA 2006: Sixth International Conference on Intelligent systems Design and applications

作者： Tan, Ying Ya-ping, Yang Jian-chao, Zeng Division of System Simulation and Computer Application Taiyuan University of Science and Technology 030024 China

ISBN: (纸本)0769525288

Particle swarm optimization (PSO) is swarm-based stochastic optimization originating from artificial life and evolutionary computation. The algorithm completes the optimization through following the personal best solution of each particle and the global best value of the whole swarm.. This paper improved the standard PSO's evolution equation on the foundation of analyzing standard PSO's model and its mechanisms, then presents a Quadratic PSO and gives a strategy of self-adapting quadratic PSO parameters through comparing Quadratic PSO with PSO and analyzing the impact that the parameters have on the performance of algorithm. Further, this paper presents a Hybrid Particle Optimization combined their advantages on the basis of comparing Quadratic PSO with PSO. The simulation illustrates the Quadratic PSO improves the performance of the PSO and the self-adapting parameters strategy is better than the fixed parameters, and the Hybrid PSO outperformed both Standard PSO and Quadratic PSO, the experimental results show that the methods are correct and efficient © 2006 IEEE.

关键词： Global optimization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：