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Multi-source signals guiding swarm robots search

Multi-source signals guiding swarm robots search

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

作者： Xue, Songdong Zeng, Jianchao Du, Jing Yang, Xiaomei College of Electrical and Information Engineering Lanzhou University of Technology 85 Langongping Lanzhou Gansu 730050 China Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology 66 Waliu Road Taiyuan Shanxi 030024 China North Automatic Control Technology Institute China North Industries Group Corporation 351 South Sports Road Taiyuan Shanxi 030006 China

ISBN: (纸本)3642025676

Victim search with swarm robots on gas outburst spot in coal mines is investigated. Upon modeling swarm robotic system with extended particle swarm optimization, heterogeneous signals are fused by each robot independently as fitness evaluate. To fuse signals of intermittent sound of call for help, periodic radio frequency waves and continuous gas, concepts of detection-success binary logic and perceptual event are introduced into describing detection process. Based on statistical properties, detectable range, localization type and accuracy, priority is determined with information entropy-based criteria. Then the best-found position is decided in a distributed fashion through election operation. simulation conducted in closed signal propagation environment indicates the approximate relation between fusion value and distance, i.e., the nearer the robot is far away from target, the higher the fusion of signals. © 2009 Springer Berlin Heidelberg.

关键词： Swarm intelligence

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A Particle Swarm Optimization with Feasibility-based Rules for Mixed-variable Optimization Problems

A Particle Swarm Optimization with Feasibility-based Rules f...

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2009 Ninth International Conference on Hybrid Intelligent systems(第九届混合智能系统国际会议 HIS 2009)

作者： Chao-Li Sun Jian-Chao Zeng Jeng-Shyang Pan Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technolo Shenzhen Graduate School Harbin Institute of Technology Shenzhen Guangdong 518055 Department of

A Particle Swarm Optimization algorithm with feasibility-based rules (FRPSO) is proposed in this paper to solve mixed-variable optimization problems. An approach to handle various kinds of variables is discussed. Constraint handling is based on simple feasibility-based rules, not needing addinional penalty parameters and not guaranteeing to be in the feasible region at all times. Two real-world mixed-varible optimization benchmark problems are presented to evaluate the performance of the FRPSO algorithm, and it is found to be highly competitive compared to other existing stochastic algorithms.

关键词： Particle Swarm Optimization Feasibility-based rules Mixed-variables

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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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International Conference on Computational Intelligence and Security

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

ISBN: (纸本)9781424454112

As a novel population-based optimization technique, the artificial physics optimization (APO) algorithm inspired by physics is presented recently. 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.

关键词： Performance analysis Physics Topology Clustering algorithms Convergence Computational intelligence Particle swarm optimization Educational institutions Laboratories Information security

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On mass effects to artificial physics optimisation algorithm for global optimisation problems

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International Journal of Innovative Computing and Applications 2009年第2期2卷 69-76页

作者： Xie, Liping Zeng, Jianchao Cui, Zhihua 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 No. 66 Waliu Road Wanbailin District Taiyuan Shanxi 030024 China

Artificial physics optimisation (APO) algorithm is an optimisation algorithm based on physicomimetics framework. Driven by virtual force, a population of sample individuals searches a global optimum in the problem space. The mass of each individual corresponds to a user-defined function of the value of an objective function to be optimised. It is an important parameter to influence the performance of APO algorithm. Therefore, in this paper, the authors make a study on the selection principle of mass on numerical optimisation problems. According to the curvilinear style of the mass functions, they are classified into three different types of curvilinear functions: convex function, linear function and concave function. To make a deep insight, several versions of APO algorithm with different mass functions are used to solve two type benchmarks: unimodal and multimodal functions. simulation results show the mass functions with concave curve may generally obtain the satisfied solution within the allowed iterations. In addition, the performance of APO algorithm is compared with that of the modified electromagnetism-like (EM), differential evolution (DE), evolutionary algorithm (EA) and particle swarm optimisation (PSO) for multidimensional numeric benchmarks. The simulation results show that APO algorithm is competitive. Copyright © 2009 Inderscience Enterprises Ltd.

关键词： Global optimization

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Estimation of Distribution Algorithm based on copula theory

Estimation of Distribution Algorithm based on copula theory

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Congress on Evolutionary Computation

作者： Li-Fang Wang Jian-Chao Zeng Yi Hong Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Taiyuan China College of Electrical and Information Lanzhou University of Technology Lanzhou China College of Electrical and Information Engineering Lanzhou University of Technology Lanzhou China

Estimation of Distribution Algorithm (EDA) is a novel evolutionary computation, which mainly depends on learning and sampling mechanisms to manipulate the evolutionary search, and has been proved a potential technique for complex problems. However, EDA generally spend too much time on the learning about the probability distribution of the promising individuals. The paper propose an improved EDA based on copula theory (copula-EDA) to enhance the learning efficiency, which models and samples the joint probability function by selecting a proper copula and learning the marginal probability distributions of the promising population. The simulating results prove the approach is easy to implement and is validated on several problems.

关键词： Electronic design automation and methodology Probability distribution Evolutionary computation Sampling methods Gaussian distribution Computational intelligence Laboratories Eyes NP-complete problem Production

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Boid particle swarm optimisation

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International Journal of Innovative Computing and Applications 2009年第2期2卷 78-85页

作者： Cui, Zhihua Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences No. 6 Kexueyuan South Rd. Zhongguancun Haidian District Beijing 100190 China Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology No. 66 Waliu Rd. Wanbailin District Taiyuan Shanxi 030024 China

Particle swarm optimisation (PSO) is a novel population-based stochastic optimisation algorithm inspired by the Reynolds' boid model. The original biological background of boid obeys three basic simple steering rules: separation, alignment and cohesion. However, to promote a simple update equation, none of these rules of boid model is employed by PSO methodology. Due to the weakness of biological background of PSO, in this paper, a new variant of PSO, boid particle swarm optimisation (BPSO), is designed in which cohesion rule and alignment rule are both employed to improve the performance. In BPSO, each particle has two motions: divergent motion and convergent motion. For divergent motion, each particle adjusts its moving direction according to the alignment direction and the cohesion direction, as well as in convergent motion, the original update equation of the standard version of PSO is used. To make a motion transition, a threshold is introduced to make the divergent motion is employed in the first period, whereas the convergent motion is used in the final stage. To testify the efficiency, several unconstrained benchmarks are used to compare. simulation results show the proposed variant is more effective and efficient than other two variants of PSO when solving multi-modal high-dimensional numerical problems. Copyright © 2009 Inderscience Enterprises Ltd.

关键词： Alignment

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An Improved Particle Swarm Optimization with Feasibility-Based Rules for Mixed-Variable Optimization Problems

An Improved Particle Swarm Optimization with Feasibility-Bas...

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

作者： Chao-Li Sun Jian-Chao Zeng Jeng-Shyang Pan Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Taiyuan Shanxi China Shenzhen Graduate School Harbin Institute of Technology Shenzhen Guangdong China Department of Electronic Engineering National Kaohsiung University of Applied Sciences Kaohsiung Taiwan

This paper presents an improved particle swarm optimization algorithm with feasibility- based rules (FRIPSO) to solve mixed-variable constrained optimization problems. Different kinds of variables are dealt in different ways in FRIPSO algorithm. Constraint handling is based on simple feasibility-based rules without the use of a penalty function which is frequently cumbersome to parameterize, nor need it to guarantee the particles be in the feasible region at all time which turn out to cost much time sometimes. In order to improve the convergence speed of FRIPSO with the iteration growing and to find global optimum, the standard PSO is used to find a better position for the best history position of the swarm on the condition that the discrete value are same with those of g best in each iteration. Two practical benchmark mixed-variable optimization problems are tested by our FRIPSO algorithm to demonstrate the effectiveness and robustness of the proposed approach.

关键词： Particle swarm optimization Constraint optimization Optimization methods Paper technology Robustness Design optimization Switches Stochastic processes Control systems Chaos

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Variable structure controller based on extended state observer in gun turret system

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Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of technology 2009年第4期29卷 318-322页

作者： Chen, Jie Jin, Tian-Xu Bai, Yong-Qiang Department of Automatic Control School of Information and Science Beijing Institute of Technology Beijing 100081 China Key Laboratory of Complex System Intelligent Control and Decision of Ministry of Education Beijing Institute of Technology Beijing 100081 China

Considering the characteristics of the servo system, such as disturbance, nonlinearity and high inertia, variable structure controller (VSC) based on extended state observers (ESO) are designed for the aim of quick, stable and high-accuracy controlling processes. The disturbances and high-order factors are estimated and compensated using ESO. Dynamic compensation reduces the motion system to approximately a triple integrator which can be easily controlled using VSC. Because ESO provides a high-frequency path for the dynamic state not modelled, the chatter of VSC is reduced. Through tests in the gun turret system, it was shown that this controller is superior to the engineering PID and general VSC.

关键词： Variable structure control

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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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Extraction of scandium from red mud by modified activated carbon and kinetics study

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Rare Metals 2008年第3期27卷 223-227页

作者： ZHOU Hualei LI Dongyan TIAN Yajun CHEN Yunfa State Key Laboratory of Multi-phase Complex System lnstituteqfProcess Engineering Chinese Academy of Sciences Beijing 100080 China School of Applied Sciences University of Science and Technology Beo＇ing Beijing 100083 China

Activated carbon （AC） was modified by tri-butyl phosphate （TBP） for selectively extracting scandium from red mud and characterized by BET （Brunauer-Emmett-Teller） surface area. The modified AC had a preferential adsorption to scandium. The influences of adsorbent dos- age, adsorption temperature, and time on adsorption capacity and selectivity to scandium were examined. An optimum adsorbent dosage （-6.25 g/L）, adsorption temperature （308 K）, and adsorption time （40 min） were figured out. A pseudo-second-order kinetics model was employed for describing the adsorption process of scandium.

关键词： modified activated carbon adsorption red mud scandium

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