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Quantum Gaussian Particle Swarm Optimization approach for PID controller design in AVR system

Quantum Gaussian Particle Swarm Optimization approach for PI...

引用

2008 IEEE International Conference on systems, Man and Cybernetics, SMC 2008

作者： Coelho, Leandro Dos Santos Herrera, Bruno Á Vila De Meirelles Industrial and Systems Engineering Graduate Program PPGEPS Pontifical Catholic University of Parana Imaculada Conceicao 1155 80215-901 Curitiba Parana Brazil

During the history of science of computational intelligence, many evolutionary algorithms approaches were proposed having more or less success in solving various optimization problems. In this context, the Particle Swarm Optimization (PSO) is a bio-inspired optimization mechanism based on the metaphor of social behaviour of birds flocking and fish schooling in search for food. Inspired by the classical PSO method and quantum mechanics theories, this work presents a Quantum-behaved PSO (QPSO) approach using Gaussian probability distribution function (G-QPSO). Numerical simulations based on optimized proportional-integral-derivative (PID) control of an automatic regulator voltage system for nominal system parameters and step reference voltage input demonstrate the effectiveness and efficiency of G-QPSO approach. Simulation results of G-QPSO to determine the PID parameters are compared with the classical PSO and QPSO. © 2008 IEEE.

关键词： Quantum theory

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Discrete Differential Evolution with Local Search to Solve the Traveling Salesman Problem: Fundamentals and Case Studies

Discrete Differential Evolution with Local Search to Solve t...

引用

IEEE International Conference on Cybernetic Intelligent systems

作者： Joao Guilherme Sauer Leandro dos Santos Coelho Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana (PPGEPS/PUCPR)

ISBN: (纸本)9781424429141

Traveling Salesman Problem (TSP) is one of the most difficult problems in the Combinatorial Optimization area. The goal of TSP is to find one path that can travel between all the nodes (instances) of the graph just once (Hamiltonian tour) in the smallest tour, that is, smallest Euclidian distance. In this context, many other meta-heuristics techniques based on evolutionary algorithms have been propose in literature to solve TSP problems. Differential Evolution (DE) is a relatively new simple evolutionary algorithm, which is an effective adaptive approach to global optimization over continuous search spaces. The design principles of DE are simplicity and efficiency. The most distinct feature of DE is that it mutates vectors by adding weighted, random vector differentials to them. Since its invention, DE has been applied with high success on many numerical optimization problems outperforming other more popular meta-heuristics such as the genetic algorithms. Recently, some researchers extended with success the application of DE to complex combinatorial optimization problems with discrete decision variables such as the traveling salesman problem, the machine layout problem, the flow-shop scheduling problem. In this paper, the following discrete DE approaches for the TSP are proposed and evaluated: i) DE approach without local search, ii) DE with local search based on Lin-Kernighan-Heulsgaun (LKH) method, and iii) DE with local search based on Variable Neighborhood Search (VNS) and together with LKH method. Numerical study is carried out using the TSPLIB of test TSP problems. In this context, the computational results are compared with the other results in the recent TSP literature. The obtained results show that LKH method is the best method to reach optimal results for TSPLIB benchmarks, but for largest problems, the ED+VNS improve the quality of obtained results.

关键词： Optimization Traveling salesman problem Evolutionary Algorithm Differential Evolution Variable Neighbor Search Local Search Lin-Kernighan-Heulsgaun travelling salesman problems evolutionary algorithm MT design theory continuous search

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Integrating agents and soft computing in Intelligent Manufacturing System models

Integrating agents and soft computing in Intelligent Manufac...

引用

International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Luiz Marcio Spinosa Leandro dos Santos Coelho Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Paraná Curitiba Parana Brazil

This paper introduces an approach to deal with IMS (Intelligent Manufacturing System) models, in order to help the development of Decision Support systems, Simulators and Performance Evaluators. The complexity of such systems is growing continuously requiring knowledge and information exchange between different manufacturing functions with more and more efficiency. This paper explores some potentialities of soft computing approaches and intelligent agent-oriented design including artificial neural networks, fuzzy systems, and evolutionary computation. The rationale is in order to support decision making processes performed by the autonomous and co-operative units of an IMS.

关键词： Computational modeling Manufacturing Object oriented modeling Artificial intelligence Artificial neural networks Evolutionary computation Fuzzy logic

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Quantum Gaussian particle swarm optimization approach for PID controller design in AVR system

Quantum Gaussian particle swarm optimization approach for PI...

引用

IEEE International Conference on systems, Man and Cybernetics

作者： Leandro dos Santos Coelho Bruno Avila de Meirelles Herrera Industrial and Systems Engineering Graduate Program PPGEPS Pontifical Catholic University of Paraná Curitiba Parana Brazil

During the history of science of computational intelligence, many evolutionary algorithms approaches were proposed having more or less success in solving various optimization problems. In this context, the Particle Swarm Optimization (PSO) is a bio-inspired optimization mechanism based on the metaphor of social behaviour of birds flocking and fish schooling in search for food. Inspired by the classical PSO method and quantum mechanics theories, this work presents a quantum-behaved PSO (QPSO) approach using Gaussian probability distribution function (G-QPSO). Numerical simulations based on optimized proportional-integral-derivative (PID) control of an automatic regulator voltage system for nominal system parameters and step reference voltage input demonstrate the effectiveness and efficiency of G-QPSO approach. Simulation results of G-QPSO to determine the PID parameters are compared with the classical PSO and QPSO.

关键词： Particle swarm optimization Three-term control Control systems Quantum mechanics History Computational intelligence Evolutionary computation Birds Marine animals Educational institutions

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Cultural differential evolution approach to optimize the economic dispatch of electrical energy using thermal generators

Cultural differential evolution approach to optimize the eco...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Leandro dos Santos Coelho Adriano Del Vigna de Almeida Viviana Cocco Mariani Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Paraná Curitiba Parana Brazil Mechanical Engineering Program Pontifical Catholic University of Paraná Curitiba Parana Brazil

Differential evolution (DE) is a powerful population-based algorithm of evolutionary computation field designed for solving global optimization problems. The potentialities of DE are its simple structure, easy use, convergence speed and robustness. However, the control parameters and learning strategies involved in DE are highly dependent on the problems under consideration. Choosing suitable parameter values requires also previous experience of the user. Despite its crucial importance, there is no consistent methodology for determining the control parameters of a DE. In this paper, different DE approaches combined with a cultural algorithm technique based on normative and situational knowledge are proposed as alternative methods to solving the economic load dispatch problem of thermal generators with valve-point effect. The DE approaches are validated for a test system consisting of 13 thermal generators whose nonsmooth fuel cost function takes into account the valve-point loading effects. Numerical results indicate that performance of the cultural DE present best results when compared with previous optimization approaches in solving load dispatch problems with the valve-point effect.

关键词： Cultural differences Optimization Evolution (biology) Generators Economics Mathematical model Evolutionary computation

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Integration of computer based problem solving into engineering curricula

Integration of computer based problem solving into engineeri...

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2008 ASEE Annual Conference and Exposition

作者： Raubenheimer, Dianne Brent, Rebecca Joines, Jeff Craig, Amy North Carolina State University Department of Adult and Higher Education College of Education NCSU Education Designs Inc. Education Designs Inc. Cary NC United States College of Engineering North Carolina State University National Effective Teaching Institute Chemistry and Science Department NC State University Textile Engineering Program College of Engineering Student-Owned Computing College of Engineering Department of Industrial and Systems Engineering NC State University

The primary objectives of this engineering project are (1) to examine how to develop students' problem solving and computational skills early in their program of study and (2) to further enhance these skills by building upon critical computing concepts semester after semester. The project is a component of NC State University's quality enhancement plan, which focuses on the use of technology in enhancing student learning. The project stems from new introductory computer-based modeling courses that were created in two engineering departments, and has expanded to include other departments. We give an overview of the project, provide an example of how a problem is modeled and broken apart, present some assessment results, and discuss the emerging lessons being learned. © American Society for engineering Education, 2008.

关键词： Problem solving

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Recommending mobile content using content diffusion and user preference transition

Recommending mobile content using content diffusion and user...

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2008 International Conference on e-Learning, e-Business, Enterprise Information systems, and e-Government, EEE 2008

作者： Lee, Tong Queue Lee, Eunchang Park, Young Park, Yongtae Department of Mobile Internet Dongyang Technical College Seoul Korea Republic of Graduate Program in Technology and Management Seoul National University Seoul Korea Republic of Department of Computer Science and Information Systems Bradley University Peoria IL United States Department of Industrial Engineering Seoul National University Seoul Korea Republic of

ISBN: (纸本)1601320639

Recommendation accuracy is especially important in mobile e-commerce environments due to the limited screen size of mobile devices and relatively expensive connection costs. Mobile content tends to be fashionable and are geared for young users. This paper presents a novel method of building a more accurate recommender system for mobile content in a mobile ecommerce environment. The method is based on collaborative filtering, and models content diffusion and user preference transition and incorporates them in constructing pseudo ratings from implicit feedback data. In a variety of experiments, recommender systems based on the method showed significantly better recommendation accuracy than a pure collaborative filtering-based recommender system.

关键词： Collaborative filtering

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DLM: A distributed Large Memory System using remote memory swapping over cluster nodes

DLM: A distributed Large Memory System using remote memory s...

引用

IEEE International Conference on Cluster computing

作者： Hiroko Midorikawa Motoyoshi Kurokawa Ryutaro Himeno Mitsuhisa Sato Department of Computer and Information Science Seikei University Musashino Tokyo Japan Graduate School of Systems and Information Engineering University of Tsukuba Tennodai Tsukuba Ibaraki Japan Advanced Center of Computing and Communication Wako Saitama Japan Research Program for Computational Science RIKEN Wako Saitama Japan

Emerging 64 bitOSpsilas supply a huge amount of memory address space that is essential for new applications using very large data. It is expected that the memory in connected nodes can be used to store swapped pages efficiently, especially in a dedicated cluster which has a high-speed network such as 10 GbE and Infiniband. In this paper, we propose the distributed large memory system (DLM), which provides very large virtual memory by using remote memory distributed over the nodes in a cluster. The performance of DLM programs using remote memory is compared to ordinary programs using local memory. The results of STREAM, NPB and Himeno benchmarks show that the DLM achieves better performance than other remote paging schemes using a block swap device to access remote memory. In addition to performance, DLM offers the advantages of easy availability and high portability, because it is a user-level software without the need for special hardware. To obtain high performance, the DLM can tune its parameters independently from kernel swap parameters. We also found that DLMpsilas independence of kernel swapping provides more stable behavior.

关键词： Kernel Arrays Memory management Servers Bandwidth Performance evaluation Benchmark testing

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Economic dispatch optimization using hybrid chaotic particle swarm optimizer

Economic dispatch optimization using hybrid chaotic particle...

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2007 IEEE International Conference on systems, Man, and Cybernetics, SMC 2007

作者： Dos Santos Coelho, Leandro Mariani, Viviana Coceo Industrial and Systems Engineering Graduate Program - PPGEPS Automation and Systems Laboratory Pontifical Catholic University of Paraná Imaculada Conceição 1155 80215-901 Curitiba Paraná Brazil Pontifical Catholic University of Paraná Mechanical Engineering Graduate Program PUCPR/PPGEM Imaculada Conceição 1155 80215-901 Curitiba Paraná Brazil

ISBN: (纸本)1424409918

Particle swarm optimization (PSO) is a population-based stochastic optimization technique, originally developed by Eberhart and Kennedy, inspired by simulation of a social psychological metaphor instead of the survival of the fittest individual. In PSO, the system (swarm) is initialized with a population of random solutions (particles) and searches for optima using cognitive and social factors by updating generations. PSO has been successfully applied to a wide range of applications, mainly in solving continuous nonlinear optimization problems. Based on the PSO and chaos theories, this paper discusses the use of a chaotic PSO approach hybridized with an Implicit Filtering (IF) technique to optimize performance of economic dispatch problems. The chaotic PSO with chaos sequences is the global optimizer and the IF is used to fine-tune the chaotic PSO run in sequential manner. The hybrid methodology is validated for a test system consisting of 13 thermal units whose incremental fuel cost function takes into account the valve-point loading effects. © 2007 IEEE.

关键词： Particle swarm optimization (PSO)

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On selecting the MIMO Generalized Orthonormal Basis Functions poles by using Particle Swarm Optimization

On selecting the MIMO Generalized Orthonormal Basis Function...

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European Control Conference (ECC)

作者： Bruno C. Reginato Gustavo H. C. Oliveira Industrial and Systems Engineering Graduate Program (PPGEPS) of the Pontifical Catholic University of Parana (PUCPR)

This paper deals with the problem of finding the pole(s) in Orthonormal Basis Functions (OBF) models. In fact, any stable system can be exactly represented by an infinite orthonormal functions series, assuming that the basis used in such representation is complete. However, in practice a truncated basis functions expansion is used. For certain basis choice and fixed number of functions, there is a pole or set of poles that minimizes the truncation error. The problem is then to find such set. Therefore, the present paper is focused on the problem of finding the basis functions poles when more than one dynamic is involved, for instance, in SISO Generalized Orthonormal Basis Functions (GOBF) models or MIMO Laguerre, Kautz and/or GOBF models. It is proposed and analyzed here the use of Particle Swarm Optimization (PSO) for such problem. Simulation results presented shows the validity of the proposed method.

关键词： Convergence Mathematical model Particle swarm optimization Approximation methods Optimization MIMO Equations

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