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International Conference on Robot, Vision and Signal Processing (RVSP)

作者： Chaoli Sun Jianchao Zeng Jengshyang Pan Yunqiang Zhang Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Taiyuan China Department of Electronic Engineering National Kaohsiung University of Applied Sciences Kaohsiung Taiwan

A new algorithm (called CPMPSO for short), in which PSO with constraint-preserving mechanism is used as a global search algorithm and PSO itself is used as local search one, is proposed in this paper to solve mixed-variable optimization problems. The values of non-continuous variables are got according to the velocity of the particle, the constraint-preserving method is used as the mechanism for handling the constraint violations, and the particle swarm optimization itself is used as local search algorithm to obtain the consistent optimal results for mixed-variable optimization problems. The performance of CPMPSO is evaluated against two real-world mixed-variable optimization problems, and it is found to be highly competitive compared with other existing algorithms.

关键词： Optimization Particle swarm optimization Programming Springs Stochastic processes Genetic algorithms

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The structure optimization of main beam for bridge crane based on an improved PSO

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Journal of Computers 2011年第8期6卷 1585-1590页

作者： Sun, Chaoli Tan, Ying Zeng, Jianchao Pan, Jengshyang Tao, Yuanfang Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Taiyuan Shanxi 030024 China Department of Electronic Engineering National Kaohsiung University of Applied Sciences Kaohsiung 807 Taiwan Mechanical and Electronic Engineering College Taiyuan University of Science and Technology Taiyuan Shanxi 030024 China

The structure optimization of main beam is a nonlinear constrained optimization problem, which is important for bridge crane to save manufacturing cost on quality assurance. The modified particle swarm optimization (MPSO) with feasibility-based rules [1], which was advanced to solve mixed-variable optimization problems, is proposed to optimize the structure of main beam in order to find the optimal parameters so as to make minimize the deadweight of main beam. The comparison results with the enumeration algorithm illustrated that MPSO can get best optimal solutions in much less calculation numbers. © 2011 ACADEMY PUBLISHER.

关键词： Quality assurance

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Differential evolution with adaptive population size combining lifetime and extinction mechanisms

Differential evolution with adaptive population size combini...

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Asian Control Conference

作者： Chunmei Zhang Jie Chen Bin Xin Tao Cai Chen Chen School of Electronic Information Engineering Taiyuan University of Science and Technology Shanxi China Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education Beijing China School of Automation Beijing Institute of Technology Beijing China

ISBN: (纸本)9781612844879

Differential Evolution (DE) is a simple and efficient numerical optimization method. Most DE variants in the literature adopt fixed population size. This paper incorporates into DE the mechanisms of lifetime and extinction which regulate DE's population size in an adaptive manner. The population size is adjusted according to the online progress of fitness improvement. Two schemes of inserting new individuals are proposed to match different mechanisms respectively. The performance of these innovations is examined through the optimization of benchmark problems. The results show that the proposed adaptive population sizing strategy is efficient for improving the convergence and efficiency of the DE.

关键词： Convergence Optimization Benchmark testing Cloning Automation Evolutionary computation Algorithm design and analysis

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Solving Constrained Optimization Problems by an Improved Particle Swarm Optimization

Solving Constrained Optimization Problems by an Improved Par...

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International Conference on Innovations in Bio-inspired Computing and Applications (IBICA)

作者： Chaoli Sun Jianchao Zeng Shuchuan Chu John F. Roddick Jengshyang Pan Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Taiyuan Shanxi China School of Computer Science Engineering and Mathematics Flinders University Australia Department of Electronic Engineering National Kaohsiung University of Applied Sciences Kaohsiung Taiwan

Constrained optimization problems compose a large part of real-world applications. More and more attentions have gradually been paid to solve this kind of problems. An improved particle swarm optimization (IPSO) algorithm based on feasibility rules is presented in this paper to solve constrained optimization problems. The average velocity of the swarm and the best history position in the particle's neighborhood are introduced as two turbulence factors, which are considered to influence the fly directions of particles, into the algorithm so as not to converge prematurely. The performance of IPSO algorithm is tested on 13 well-known benchmark functions. The experimental results show that the proposed IPSO algorithm is simple, effective and highly competitive.

关键词： Optimization Particle swarm optimization Algorithm design and analysis Convergence History Educational institutions

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Research on information capability based high-performance information sharing in the simulation grid

Research on information capability based high-performance in...

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2010 International Conference on Computer Modeling and simulation, ICCMS 2010

作者： Zhang, Ying-Chao Zhang, Jing Chen, Xin Yu, Qin-Zhang Zhang, Hao Beijing Institute of System Engineering National Key Laboratory of Science and Technology on Complex Systems Simulation Beijing China

ISBN: (纸本)9780769539416

For the purpose of achieving high-performance information sharing in the simulation grid, an information capability-based Three Phase High-performance Information Sharing Approach (TPHPISA for short) is advanced. The main idea of TPHPISA is selecting those information sources with highest information capability and executing the best query plan. To illustrate how TPHPISA works, an application example of implementing high-performance information sharing in the stock market simulation grid is thoroughly analyzed. At last, the searching efficiency and accuracy of TPHPISA are demonstrated through comparing with other similar method. © 2010 IEEE.

关键词： Information analysis

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Modeling and simulation of international politics evolution in crisis

Modeling and simulation of international politics evolution ...

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2010 International Conference on Computer Modeling and simulation, ICCMS 2010

作者： Yu, Qin-Zhang Zhang, Jing Zhang, Ying-Chao Beijing Institute of System Engineering National Key Laboratory of Science and Technology on Complex Systems Simulation Beijing China

ISBN: (纸本)9780769539416

Under the guidance of the theories of complex Adaptive system and Artificial Societies, modeling and simulation of international politics evolution in crisis was studied. The Model of International Politics Evolution (IPEM) in crisis based on Agent was built. The function and architecture of IPEM were introduced. Individual Agent , including Nation Agent, International Organization Agent and Environment Agent, was described in detail. A simulation experiment was executed to prove the effectiveness of the method proposed in the paper. © 2010 IEEE.

关键词： Adaptive systems

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Research on multi-agent based C4ISR effectiveness simulation and evaluation

Research on multi-agent based C4ISR effectiveness simulation...

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2010 International Conference on Computer Modeling and simulation, ICCMS 2010

作者： Zhang, Ying-Chao Feng, Ye Yu, Qin-Zhang Xin, Chen Beijing Institute of System Engineering National Key Laboratory of Science and Technology on Complex Systems Simulation Beijing China

ISBN: (纸本)9780769539416

The effectiveness evaluation of C4ISR is playing a more and more important role in its designing, developing and using. According to its characters, such as complexity and intelligence, the traditional evaluation methods and systems are not feasible and effective enough. In this paper, a multiagent based simulation and evaluation framework of C4ISR is advanced, which includes the architecture and the working procedure. The agent-based simulation model of C4ISR is put forward, according to which the C2 Agent, Armament Agent and the Evaluation Agent are founded. The interaction model between each agent is also designed. At final, based on the Swarm, a multi-agent based C4ISR effectiveness simulation and evaluation prototype is developed, through which NATO C2 effectiveness evaluation is thoroughly analyzed. © 2010 IEEE.

关键词： Multi agent systems

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RETRACTED ARTICLE: Crank block steering mechanism optimization for forklift truck based on PSO

RETRACTED ARTICLE: Crank block steering mechanism optimizati...

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作者： Sun, Chaoli Zeng, Jianchao Pan, Jengshyang Tao, Yuanfang Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Taiyuan Shanxi 030024 China Department of Electronic Engineering National Kaohsiung University of Applied Sciences Kaohsiung 807 Taiwan Mechanical and Electronic Engineering College Taiyuan University of Science and Technology Taiyuan Shanxi 030024 China

ISBN: (纸本)9781424463503

Crank block steering mechanism optimization is a nonlinear constrained optimization problem, which is important for forklift truck to get preferable steering performance. Particle swarm optimization (PSO) with feasibility-based rules is a swarm intelligent algorithm proposed to solve constrained optimization problems simply and effectively. Therefore, it is used to optimize crank block steering mechanism of forklift truck, which will try to minimize the maximal error of outer wheel steering angle, maximize the minimal transmission angle and improve the force transmission. Experimental results obtained by PSO with feasibility-based rules are compared with those obtained by enumeration algorithm which is reliable for optimization problems. The comparison results showed that particle swarm optimization with feasibility-based rules can get same optimal results as enumeration algorithm in much less calculation times. © 2010 IEEE.

关键词： Particle swarm optimization (PSO)

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Research on Multi-agent Based C4ISR Effectiveness simulation and Evaluation

Research on Multi-agent Based C4ISR Effectiveness Simulation...

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International Conference on Computer Modeling and simulation, ICCMS

作者： Zhang Ying-chao Ye Feng Yu Qin-zhang Chen Xin National Key Laboratory of Science and Technology on Complex Systems Simulation Beijing Institute of System Engineering Beijing China

The effectiveness evaluation of C4ISR is playing a more and more important role in its designing, developing and using. According to its characters, such as complexity and intelligence, the traditional evaluation methods and systems are not feasible and effective enough. In this paper, a multi-agent based simulation and evaluation framework of C4ISR is advanced, which includes the architecture and the working procedure. The agent-based simulation model of C4ISR is put forward, according to which the C2 Agent, Armament Agent and the Evaluation Agent are founded. The interaction model between each agent is also designed. At final, based on the Swarm, a multi-agent based C4ISR effectiveness simulation and evaluation prototype is developed, through which NATO C2 effectiveness evaluation is thoroughly analyzed.

关键词： Analytical models Computational modeling Computer simulation Adaptive systems Intelligent agent Design engineering systems engineering and theory Laboratories Virtual prototyping Information technology

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Effects of algorithmic parameters on swarm robotic search

Effects of algorithmic parameters on swarm robotic search

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2010 IEEE International Conference on Information and Automation, ICIA 2010

作者： Tan, Ying Xue, Songdong Zeng, Jianchao Pan, Jengshyang Pan, Tienszu Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology No. 66 Waliu Road Taiyuan Shanxi 030024 China Department of Electronic Engineering National Kaohsiung University of Applied Sciences Kaohsiung 807 Taiwan

ISBN: (纸本)9781424457021

As one of valid modeling and coordinated controlling tools, the particle swarm optimization algorithm can be extended for applying to task of swarm robotic search. To gain an insight into effects of key parameters on distributed swarm search, a series of simulations are conducted. In such control algorithm, main parameters including communication range, detection radius, and swarm size are paid close attentions. Similar to ideal "particles", swarm robots are modeled at an abstract level with the extended particle swarm optimization method. Also, individual robots are designed to be controlled under three-state finite state machine mechanism having SingleSearch, SwarmSearch, and Declaration states. Then, control strategy and algorithm are developed. The statistical results from simulating data indicate the validity of modeling approach. Further, the effects of three parameters on overhead and efficiency of system running come out in compared analysis both of different size swarms and of specific size swarm. ©2010 IEEE.

关键词： Swarm intelligence

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