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Individual predicted integral-controlled particle swarm optimisation

International Journal of Innovative Computing and applications 2009年第2期2卷 115-122页

作者： Cai, Xingjuan Tan, Ying Division of System Simulation and Computer Application Taiyuan University of Science and Technology 030024 China

Integral-controlled particle swarm optimisation (ICPSO) is a novel variant of particle swarm optimisation by incorporating two integral controllers into the methodology. Although the population diversity of ICPSO is large enough, the performance is not always well when dealing with multi-modal problems, the reason is partly due to the wrong guiding direction dominated by the best location found by entire swarm. Therefore, in this paper, a new strategy, quadratic interpolation method, is introduced to estimate the exact local optima in the best location neighbours. simulation results show this new variant provide an efficient local search capability than other three variants of particle swarm optimisation when solving multi-modal numerical problems. Copyright © 2009 Inderscience Enterprises Ltd.

关键词： Particle swarm optimization (PSO)

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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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An enhanced hybrid quadratic particle swarm optimization

An enhanced hybrid quadratic particle swarm optimization

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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

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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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The research of pump truck MRO system based on the internet of things

The research of pump truck MRO system based on the internet ...

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2012 International Conference on Computing, Measurement, Control and Sensor Network, CMCSN 2012

作者： Shi, Hui Dong, Zengshou Chang, Chunbo Division of System Simulation and Computer Application Taiyuan University of Science and Technology Taiyuan China

ISBN: (纸本)9780769547381

In view of the application scenarios and demand of mechanical equipment pump truck, design a pump truck MRO system based on the Internet of Things and Cloud Computing, and the overall structure, General structure are given, and the system function is designed. Using the system, the functions can be realized, such as pump truck remote data acquisition, diagnosis processing and production management. © 2012 IEEE.

关键词： Repair

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Cooperative particle swarm optimization for steel making scheduling

Cooperative particle swarm optimization for steel making sch...

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3rd International Conference on Natural Computation, ICNC 2007

作者： Huimin, Gao Jianchao, Zeng Division of System Simulation and Computer Application Taiyuan University of Science and Technology Shanxi 030024 China

ISBN: (纸本)0769528759

0-1 Integer programming model of steel making scheduling was presented. Boolean algebra was applied to restrict the search space of Particle Swarm Optimization to 0-1 space. As for the difficulty of constraint problem of 0-1 integer programming, a Cooperative Particle Swarm Optimization(CPSO) algorithm was proposed, which embody two particle swarms, one's goal is to minimize the objective function, the other is to meet the constraint condition. Two particle swarms cooperate to solve the 0-1 integer programming problem. The CPSO algorithm has been applied to solve the steel making scheduling problem successfully. © 2007 IEEE.

关键词： Global optimization

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Research of asynchronous communication messaging model based on time decoupling in distributed object-oriented middleware

Research of asynchronous communication messaging model based...

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SNPD 2007: 8th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing

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

ISBN: (纸本)0769529097

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. © 2007 IEEE.

关键词： Message passing

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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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Hybrid heuristics scheduling algorithm for hot strip mill

Hybrid heuristics scheduling algorithm for hot strip mill

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3rd International Conference on Natural Computation, ICNC 2007

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

ISBN: (纸本)0769528759

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. © 2007 IEEE.

关键词： Heuristic algorithms

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Improved discrete particle swarm optimization based on edge coding and multilevel reduction strategy for larger scale TSP

Improved discrete particle swarm optimization based on edge ...

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2010 6th International Conference on Natural Computation, ICNC'10

作者： Jie, Jing Ji, Huanhuan Wang, Meng Zhao, Ming Computer Science and Technology College Zhejiang University of Technology Hangzhou China Division of System Simulation and Computer Application Taiyuan University of Science and Technology Taiyuan City China

ISBN: (纸本)9781424459612

In order to improve particle swarm optimization (PSO) to solve complex and large scale discrete optimization problems, the paper introduces an improved discrete particle swarm optimization (IDPSO) based on edge coding and multilevel reduction strategy. First, redefine the velocity and position of the particle based on edge-coding, and adopte converted insertion of the edge to update the information of the particle. Next, introduce multilevel reduction strategy to improve the global convergence ability of the algorithm. The improved algorithm is applied to solve representative cases of traveling salesman problem (TSP), The results show that the improved DPSO is efficient and valid on the complex TSP, and the multilevel reduction strategy can contribute to develop the ability of PSO when facing up to the complex and large scale problems greatly. © 2010 IEEE.

关键词： Traveling salesman problem

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