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3rd International Conference on Genetic and Evolutionary Computing, WGEC 2009

作者： Yang, Xiaomei Zeng, Jianchao Liang, Jiye Complex System and Computer Intelligence Laboratory Taiyuan University of Science and Technology Taiyuan China College of Management Shanxi University Taiyuan China

ISBN: (纸本)9780769538990

Through analyzing the present genetic operators to solve the Job Shop Scheduling Problem, a inversion order number Genetic Algorithm is proposed. In view of the quality of the inversion order number, this algorithm measures the population diversity by the relative inversion order number. It uses the information provided by the inversion order number of the individual and the offspring is generated. This algorithm not only satisfies the characteristic of the Job Shop Scheduling Problem, but also develops the search capacity of Genetic Algorithm. The computation results validate the effectiveness of the proposed algorithm. © 2009 IEEE.

关键词： Genetic algorithms

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Preface

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Fuel Processing technology 2010年第8期91卷 799-799页

作者： Guangwen Xu Chun-Zhu Li Kinya Sakanishi State Key Laboratory of Multiphase Complex System Institute of Process Engineering Chinese Academy of Sciences China Curtin Centre for Advanced Energy Science and Engineering Curtin University of Technology Australia Biomass Technology Research Center National Institute of Advanced Industrial Science and Technology Japan

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Design and Implementation of the Federation Load Simulator

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系统仿真技术 2009年第1期5卷 23-27页

作者：张静张柯 State Key Laboratory for Complex System Simulation Beijing Institute of System Engineer Mechatronics and Automation School National University of Defense Technology

The importance and necessity of emulating the federation load during simulating was analyzed ***,the special federation load emulation tool,federation load simulator (FLS),was designed and implemented,by which all of the vital essential characteristics of a simulation could be tested,*** amount of federates,the joint/resigned speed of federate,the amount of object instances,the registered/deleted speed of instance in one single program,etc..The applications proved that FLS could provide a convenient,effective and adjustable simulation load testing environment during the procedure of run-time infrastructure(RTI)and interrelated tool federates researching,developing and performance ***,the FLS utilized all kinds of resources with high efficiency.

关键词：仿真模拟技术后勤系统自动化系统管理模式

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An extended artificial physics optimization algorithm for global optimization problems

An extended artificial physics optimization algorithm for gl...

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2009 4th International Conference on Innovative Computing, Information and Control, ICICIC 2009

作者： Xie, Liping Zeng, Jianchao College of Electrical and Information Engineering Lanzhou University of Technology Lanzhou 730050 China Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Taiyuan Shanxi 030024 China

ISBN: (纸本)9780769538730

Artificial Physics Optimization (APO) algorithm inspired by natural physical forces is a population-based stochastic algorithm based on Physicomimetics framework. In this paper, an extended APO (EAPO) algorithm is presented through considering the personal best positions of all individual, which can provide much useful information for search. In EAPO algorithm, the velocity updated equation is similar to that of PSO algorithm. By comparison and analysis, we can consider that EAPO algorithm is a general form of PSO algorithm and has a better diversity than PSO algorithm. The simulation results confirm that EAPO is an effective stochastic population-based search algorithm. Meanwhile, a comparison with other population-based heuristics shows that EAPO algorithm is competitive. © 2009 IEEE.

关键词： Global optimization

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Performance analysis of the artificial physics optimization algorithm with simple neighborhood topologies

Performance analysis of the artificial physics optimization ...

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2009 International Conference on Computational Intelligence and Security, CIS 2009

作者： Mo, Simin Zeng, Jianchao College of Electrical and Information Engineering Lanzhou University of Technology Lanzhou Ganshu 730050 China Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Taiyuan Shanxi 030024 China

ISBN: (纸本)9780769539317

As a novel population-based optimization technique, the artificial physics optimization (APO) algorithm inspired by physics is presented recently[1-4]. Although it is characteristic of rapid convergence speed, it also suffers from worse diversity and premature convergence. Accordingly, drawing lessons from those strategies for interactions among individuals in other algorithms, this paper presents the local artificial physics optimization (LAPO) algorithm both to apply it under some simply topologies and to get an insight into the effect of structures. For this end, the performances of LAPO algorithm under particular topologies are investigated by the gravitation constant G adjusting. simulation results show that LAPO algorithm is valid under some neighborhood structures and that the gravitation constant G has a great influence on performance of LAPO algorithm with different topologies. Also, the results from simulation indicate that the presented LAPO algorithm is superior to APO algorithm so long as parameter G is selected properly under particular topologies. © 2009 IEEE.

关键词： Topology

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Template-free Synthesis of a Novel 3D Cage-like Nanostructured α-Fe_2O_3

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Chemical Research in Chinese Universities 2009年第6期25卷 963-965页

作者： YU Ran-bo LI Zhen-min WANG Dan XING Chao-jian LAI Xiao-yong XING Xian-ran Department of Physical Chemistry University of Science and Technology Beijing Beijing 100083 P. R. China State Key Laboratory of Multi-phase Complex System Institute of Process Engineering Chinese Academy of Sciences Beijing 100190 P. R. China Key Laboratory of Ecologic ＆ Recycle Metallurgy Ministry of Education University of Science and Technology Beijing Beijing 100083 P. R. China

Synthesis of inorganic nanostructures with specific size and well defined morphologies has attracted considerable attention due to their superior electrical, optical, magnetic, and chemical properties. Up to now, various kinds of metal oxide, sulfide, and hydrate with controlled hierarchical and complex morphologies have been successfully synthesized.

关键词： Template-free α-Fe2O3 Nanostructure

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An improved particle swarm optimization with feasibility-based rules for constrained optimization problems

An improved particle swarm optimization with feasibility-bas...

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22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent systems, IEA/AIE 2009

作者： Sun, Chao-Li Zeng, Jian-Chao Pan, Jeng-Shyang 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

ISBN: (纸本)3642025676

This paper presents an improved particle swarm optimization (IPSO) to solve constrained optimization problems, which handles constraints based on certain feasibility-based rules. A turbulence operator is incorporated into IPSO algorithm to overcome the premature convergence. At the same time, a set called FPS is proposed to save those P best locating in the feasible region. Different from the standard PSO, g best in IPSO is chosen from the FPS instead of the swarm. Furthermore, the mutation operation is applied to the P best with the maximal constraint violation value in the swarm, which can guide particles to close the feasible region quickly. The performance of IPSO algorithm is tested on a well-known benchmark suite and the experimental results show that the proposed approach is highly competitive, effective and efficient. © 2009 Springer Berlin Heidelberg.

关键词： Particle swarm optimization (PSO)

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General framework of Artificial Physics Optimization Algorithm

General framework of Artificial Physics Optimization Algorit...

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World Congress on Nature & Biologically Inspired Computing (NaBIC)

作者： Liping Xie Jianchao Zeng Zhihua Cui Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Taiyuan Shanxi China Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Taiyuan Shanxi China

This paper presents a general framework of physics-inspired method named artificial physics optimization (APO) Algorithm, a population-based, stochastic for multidimensional search and optimization. APO invokes a gravitational metaphor in which the force of gravity may be attractive or repulsive, the aggregate effect of which is to move particles toward local and global optima. APO's particles (solutions to the optimization problem) are treated as physical individuals, each individual has a mass, position and velocity. The mass of each individual corresponds to a user-defined function of the value of an objective function to be optimized. Responding to virtual forces, APO's individuals move toward other particles with larger ¿masses¿ (better fitnesses) and away from lower mass particles (worse fitnesses). Each individual attracts all others whose mass is lower, and repels all others whose mass is greater. The individual with the greatest mass (¿best¿ individual) attracts all other individuals, and it is neither attracted to nor repelled by all the others. The attraction-repulsion rule causes APO's population to search regions of the decision space with better fitnesses. Experimental simulations show that APO is tested against several benchmark functions with better results.

关键词： Physics Optimization methods Particle swarm optimization Computational intelligence Laboratories Paper technology Electronic mail Gravity Birds Immune system

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Stochastic Dynamic Step Length Particle Swarm Optimization

Stochastic Dynamic Step Length Particle Swarm Optimization

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

作者： Xingjuan Cai Zhihua Cui Ying Tan Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Shanxi China

Stochastic particle swarm optimization is a novel variant of particle swarm optimization that convergent to the global optimum with probability one. However, the local search capability is not always well in some cases, therefore, in this paper, a technique, dynamic step length, is incorporated into the structure of stochastic particle swarm optimization aiming to further improve the performance. In this modification, each particle will adjust its velocity according to its performance. In other words, if it finds a better region, it will make a local search, otherwise, a global search pattern is given. By the way, to combining the advantages between the standard version (with better exploitation capability) and the stochastic version (with better exploration capability), the first half period is used with the standard version incorporated with dynamic step length, while in later generations, the stochastic version with dynamic step length is used to escape from a local optimum. simulation results show this strategy may provide well balance between exploration and exploitation capabilities, and improve the performance significantly.

关键词： Stochastic processes Particle swarm optimization Convergence Control systems Stochastic systems Computational intelligence Laboratories Velocity control Size control Random number generation

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Stochastic velocity threshold inspired by evolutionary programming

Stochastic velocity threshold inspired by evolutionary progr...

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World Congress on Nature & Biologically Inspired Computing (NaBIC)

作者： Zhihua Cui Xingjuan Cai Jianchao Zeng Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Shanxi China

Particle swarm optimization (PSO) is a new robust swarm intelligence technique, which has exhibited good performance on well-known numerical test problems. Though many improvements published aims to increase the computational efficiency, there are still many works need to do. Inspired by evolutionary programming theory, this paper proposes a self-adaptive particle swarm optimization in which the velocity threshold dynamically changes during the course of a simulation, and two further techniques are designed to avoid badly adjusted by the self-adaption. Six benchmark functions are used to testify the new algorithm, and the results show the new adaptive PSO clearly leads to better performance, although the performance improvements were found to be dependent on problems.

关键词： Stochastic processes Genetic programming Particle swarm optimization Computational efficiency Evolutionary computation Benchmark testing Equations Convergence Space exploration Stochastic systems

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