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检索条件"机构=Complex System and Computer Intelligence Laboratory"

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A hybrid social emotional optimization algorithm with Metropolis rule

A hybrid social emotional optimization algorithm with Metrop...

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作者： Wu, Jianna Cui, Zhihua Liu, Jing Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Taiyuan shanxi 030024 China Department of Computer Taiyuan shanxi 030024 China

ISBN: (纸本)9780956715708

Social emotional optimization algorithm (SEOA) is a novel swarm intelligent population-based optimization algorithm by simulating the human social behaviors. However, it's diversity is decreased increased when solving high-dimensional multi-modal optimization problems. Therefore, in this paper, a new hybrid SEOA with Metropolis rule is introduced to enhance the exploration capability. To test the performance, five famous benchmarks are selected, and compared with the standard version with different dimensions. Simulation results show this hybrid algorithm can increase the global search capability significantly. © 2011 Shanghai Jiaotong Univ.

关键词： Behavioral research

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On the kinematics analysis and motion planning of the manipulator of a mobile robot

On the kinematics analysis and motion planning of the manipu...

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Chinese Control and Decision Conference

作者： Qizhi, Wang De, Xu School of Computer and Information Technology Beijing Jiaotong University Beijing 100044 China Key Laboratory of Complex System and Intelligence Science Institute of Automation Chinese Academy of Sciences Beijing 100080 China

ISBN: (纸本)9781424487363

Aiming at realizing the motion planning and use of manipulator mounted on a mobile robot, the kinematical properties of an non-redundant regional manipulator with less than six joints are investigated. In order to keep good manipulability of end-effector of the mobile manipulator, the end-effector pose and workspace analysis were studied. A method by using an inversion of a workspace formulation through analytical expression to benefit manipulators synthesis design and optimization was proposed. In order to perform a grasp task in a remote area, the motion planning of the mobile manipulator was realized based on both the robot movement and the manipulator operation. Motion planning for a manipulator that grasps a small object was proposed in practical environment. The manipulator motion planning based on its workspace and coordination of two manipulators were investigated with safety considerations. © 2011 IEEE.

关键词： Kinematics

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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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2011 2nd International Conference on Innovations in Bio-inspired Computing and Applications, IBICA 2011

作者： Sun, Chaoli Zeng, Jianchao Chu, Shuchuan Roddick, John F. Pan, Jengshyang 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

ISBN: (纸本)9781457712197

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. © 2011 IEEE.

关键词： Particle swarm optimization (PSO)

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A new stochastic algorithm used to produce initial values for constrained optimization problems

A new stochastic algorithm used to produce initial values fo...

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2011 International Conference of Soft Computing and Pattern Recognition, SoCPaR 2011

作者： Zhao, Ziqiang Cui, Zhihua Zeng, Jianchao Yue, Xiaoguang Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology China Library and Information Center Shanxi University of Traditional Chinese Medicine Taiyuan 030024 China College of Computer and Information Southwest Forestry University Kunming 650224 China

ISBN: (纸本)9781457711947

In this paper, a new stochastic optimization algorithm is introduced to simulate the plant growing process. It employs the photosynthesis operator and phototropism operator to mimic photosynthesis and phototropism phenomenon. For the plant growing process, photosynthesis is a basic mechanism to provide the energy from sunshine, while phototropism is an important character to guide the growing direction. In our algorithm, each individual is called a branch, and the sampled points are regarded as the branch growing trajectory. Phototropism operator is designed to introduce the fitness function value, as well as phototropism operator is used to decide the growing direction. To test the performance, it is used to produce initial values for constrained optimization problems. Simulation results show this new algorithm increases the performance significantly when compared with method of randomly generating. © 2011 IEEE.

关键词： Photosynthesis

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Artificial plant optimization algorithm for constrained optimization problems

Artificial plant optimization algorithm for constrained opti...

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2011 2nd International Conference on Innovations in Bio-inspired Computing and Applications, IBICA 2011

作者： Zhao, Ziqiang Cui, Zhihua Zeng, Jianchao Yue, Xiaoguang Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Taiyuan 030024 China Library and Information Center Shanxi University of Traditional Chinese Medicine Taiyuan 030024 China College of Computer and Information Southwest Forestry University Kunming 650224 China

ISBN: (纸本)9781457712197

Artificial plant optimization algorithm is proposed to solve constrained optimization problems in this paper. In APOA, a shrinkage coefficient is introduce to ensure that all dimensions of a branch are within lower and upper bounds, and a new function to determine whether the particle is within the feasible region. One dimensional search optimization methods are selected in algorithm to produce a new position which is guaranteed to be in the feasible region for the branch which escapes from the feasible region. The experimental results show that artificial plant optimization algorithm is effective and efficient for constrained optimization problems. © 2011 IEEE.

关键词： Shrinkage

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A hybrid social emotional optimization algorithm with Metropolis rule

A hybrid social emotional optimization algorithm with Metrop...

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International Conference on Modelling, Identification and Control (ICMIC)

作者： Jianna Wu Zhihua Cui Jing Liu Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Taiyuan Shanxi China Department of Computer Taiyuan Shanxi China

关键词： Optimization Cultural differences Indexes Humans Solids Decision making Benchmark testing

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A new stochastic algorithm used to produce initial values for constrained optimization problems

A new stochastic algorithm used to produce initial values fo...

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International Conference of Soft Computing and Pattern Recognition (SOCPAR)

作者： Ziqiang Zhao Zhihua Cui Jianchao Zeng Xiaoguang Yue Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Taiyuan China College of Computer and Information Southwest Forestry University Kunming China

关键词： Optimization Educational institutions Vectors Particle swarm optimization Mathematical model Search problems Pattern recognition

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Artificial Plant Optimization Algorithm for Constrained Optimization Problems

Artificial Plant Optimization Algorithm for Constrained Opti...

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

作者： Ziqiang Zhao Zhihua Cui Jianchao Zeng Xiaoguang Yue Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology China Library and Information Center Shanxi University of Traditional Chinese Medicine Taiyuan China College of Computer and Information Southwest Forestry University Kunming China

关键词： Upper bound Educational institutions Algorithm design and analysis Particle swarm optimization Search problems Optimization methods

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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

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

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A genetic algorithm for job shop scheduling problem using co-evolution and competition mechanism

A genetic algorithm for job shop scheduling problem using co...

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International Conference on Artificial intelligence and Computational intelligence (AICI)

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

ISBN: (纸本)9780769542256

Through analyzing the characteristic of genetic algorithm and Job Shop scheduling Problem, a new genetic algorithm is proposed. This algorithm is based on the mechanism of co-evolution and natural selection. The parents and the genetic operators are selected by the competitive principle. Therefore, this algorithm can not only denotes parallelism in the course of GA, but also develops the solution quality to the Job Shop scheduling problem. The computation results validate the effectiveness of the proposed algorithm. © 2010 IEEE.

关键词： Genetic algorithms

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