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IEEE International Conference on systems, Man and Cybernetics

作者： Viviana Cocco Mariani Rodrigo Clemente Thom de Souza Leandro dos Santos Coelho Department of Mechanical Engineering PPGEM Pontifical Catholic University of Paraná Curitiba Parana Brazil Industrial and Systems Engineering PPGEPS Pontifical Catholic University of Paraná Curitiba Parana Brazil Industrial and Systems Engineering Graduate Program PPGEPS Pontifical Catholic University of Paraná Curitiba Parana Brazil

A new class of meta-heuristics called SOMA (Self-Organizing Migrating Algorithm) was proposed in recent literature. SOMA works on a population of potential solutions called specimen and it is based on the self-organizing behavior of groups of individuals in a ¿social environment¿. This paper proposes a modified SOMA approach to solving the economic load dispatch problem of thermal generators with the valve-point effect. To show the performance of the proposed modified SOMA algorithm based on fundamentals of normative knowledge in cultural algorithms, which was applied to test the power economic problem comprised 10 generating units with valve-point effects and multiple fuels for the load demands of 2400 MW. Simulation results show that the classical and modified SOMA algorithms are efficient and have good convergence property when compared with results of other optimization methods reported in the literature.

关键词： Power generation economics Fuel economy Power generation Environmental economics Power system economics industrial economics systems engineering and theory Stochastic processes Evolutionary computation Modeling

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Hardware Architecture for Particle Swarm Optimization Using Floating-Point Arithmetic

Hardware Architecture for Particle Swarm Optimization Using ...

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International Conference on Intelligent systems Design and Applications (ISDA)

作者： Daniel M. Muñoz Arboleda Carlos H. Llanos Leandro dos_S. Coelho Mauricio Ayala-Rincón Department of Mechanical Engineering University of Brasilia Brasília Brazil Industrial and Systems Engineering Graduate Program Pontificial Catholic University of Paraná Curitiba Brazil Departments of Computer Science and Mathematics University of Brasilia Brasília Brazil

High computational cost for solving large engineering optimization problems point out the design of parallel optimization algorithms. Population based optimization algorithms provide parallel capabilities that can be explored by their implementations done directly in hardware. This paper presents a hardware implementation of particle swarm optimization algorithms using an efficient floating-point arithmetic which performs the computations with high precision. All the architectures are parameterizable by bit-width, allowing the designer to choose the suitable format according to the requirements of the optimization problem. Synthesis and simulation results demonstrate that the proposed architecture achieves satisfactory results obtaining a better performance in therms of elapsed time than conventional software implementations.

关键词： Hardware Particle swarm optimization Floating-point arithmetic Computer architecture Design optimization Computational efficiency Design engineering Algorithm design and analysis High performance computing Computational modeling

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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...

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7th IEEE International Conference on Cybernetic Intelligent systems

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

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 now-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

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Design-dimensioning model for grid-connected photovoltaic systems

Design-dimensioning model for grid-connected photovoltaic sy...

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IEEE Canada Electric Power Conference, EPEC

作者： Johann Hernandez Nelson L. Diaz Gerardo Gordillo Laboratorio de Investigacion en Fuentes Alternativas de Energia LIFAE of Universidad Distrital F.J.C. and Grupo de Materiales Semiconductores & Energia Solar GNS&ES Universidad Nacional de Colombia Bogota Colombia Department of Electrical and computer Engineering Graduate Program in Engineering Industrial AutomationLIFAE GMS&ES Universidad Nacional de Colombia Bogota Colombia Grupo de Materiales Semiconductores & Energia Soolar GMS&ES Universidad Nacional Bogota Colombia

ISBN: (纸本)9781424445080;9781424445097

The main goal in design of grid connected photo voltaic systems is to maximize the energy generation based on local energy requirements, weather conditions, economic and social impact. These factors ought to be taken into account to perform a model that allow design grid-connected photo voltaic systems. In this paper a design-dimensioning model for a small grid-connected photo voltaic systems is developed. This model is based on energy conditions instead of power conditions. There are shown the two main trends that are typically used in the design process and the calculation of the two main components of the system (generator and inverter). An example of the design for a system located in Bogota-Colombia is studied.

关键词： Photovoltaic systems Power grids Power system modeling Mesh generation Inverters Solar power generation Power generation Power system economics Power generation economics DC generators

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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...

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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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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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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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Economic dispatch optimization using hybrid chaotic particle swarm optimizer

Economic dispatch optimization using hybrid chaotic particle...

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IEEE International Conference on systems, Man and Cybernetics

作者： Leandro dos Santos Coelho Viviana Cocco Mariani Industrial and Systems Engineering Graduate Program-PPGEPS Automation and Systems Laboratory PUCPR/PPGEPS Pontifical Catholic University of Paraná Curitiba Parana Brazil Mechanical Engineering Graduate Program PUCPR/PPGEM Pontifical Catholic University of Paraná Curitiba Parana Brazil

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.

关键词： Chaos Particle swarm optimization Fuel economy Stochastic processes Psychology Social factors Filtering theory System testing Thermal loading Cost function

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Abstracts for the 6th Congress of Asian Sleep Research Society

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Sleep and Biological Rhythms 2009年第4期7卷 A1-A49页

作者： Seog Ju Kim In Kyoon Lyoo Yu-Jin Lee Do-Un Jeong Hirohiko Kanai Masanori Ookubo Atsushi Yoshimura Masako Okawa Noto Yamada Takeshi Munezawa Yoshitaka Kaneita Yoneatsu Osaki Hideyuki Kanda Tadahiro Ohtsu Masumi Minowa Kenji Suzuki Susumu Hijuchi Hiroyuki Suzuki Takashi Ohida Takashi Abe Yoko Komada Shoichi Asaoka Yuichi Inoue Jin-Seong Lee Sang-Yong Cho Deependra Kumar Hruda N. Mallick Velayudhan M. Kumar Mahesh K. Kaushik Akihiro Karashima Noiihiro Katayama Mitsuyuki Nakao Ravindra P. Nagendra Sathiamma Sulekha Sathyaprabha T. Narasappa Pradhan Nityanadan Bindu M. Kutty M. Nirmala P. N. Ravindra Basavaraj Ramappa Tubaki T. N. Sathyaprabha D. Sudhakar G. S. Lavekar C. R. Chandrashekar Kazuyoshi Kitaoka Mika Shimizu Sachiko Chikahisa Kazuo Yoshizaki Hiroyoshi Sei Jong-Won Kim Misa Takegami Shin Yamazaki Yasuaki Hayashino Rei Ono Koji Otani Miho Sekiguchi Shinichi Konno Shunichi Fukuhara Antonia J. Jakobson Paul Fitzgerald Russell Conduit Xin Shirley Li Siu Ping Joyce Lam Wai Man Mandy Yu Yun Kwok Wing Kazue Okamoto-Mizuno Koh Mizuno Mituaki Yamamoto Shuichiro Shirakawa Hirokazu Doi Mikako Kato Kazuyuki Shinohara Masako Tamaki Shiori Matsuda Katsuo Yamazaki Tadao Hori Keiko Ogawa Kazumi Takahashi Kunio Kitahama Katuo Yamazaki Yoshimasa Koyama Kohei Shioda Yugo Ueda Yukiko Nakamura Clara Inoue Kazushige Goto Sunao Uchida Yuko Morita Yoshiko Honda Tohru Kodama Mari Hagihara Toru Nakajima Masato Saito Yoshihiko Koga Shinichiro Tanaka Hiroshi Yamadera Madoka Takahara Sachiko Suwa Minoru Onozuka Sadao Sato Fumiharu Togo Taiki Komatsu Takeshi Mitani Jongbae Choi Bin Zhang Taeko Sasai Hiromi Mitsubayashi Mitsuhiro Ohtsu Isao Hasegawa Tatsuya Ishii Akira Komori Asami Suzuki Yue Nakahara Mitsuyasu Hiroki Mariko Nakauma Nobuo Someya Tomomasa Ochiai Shouhei Komori Rayleigh Ping-Ying Chiang Zai-Ting Yeh Seong T. Kim Seung H. Yoon Jeong S. Kwon Jong H. Choi Hyung J. Ahn Youngsoo Kim Bolortuya Yunren Lichao Chen Radhika Basheer Robert W. Mccarley Robert E. Strecker Makoto Honda Tetsuaki Arai Miyuki Fukazawa Yutaka Honda Kuniaki T Department of Psychiatry Gachon University of Medicine and Science Incheon Republic of Korea Department of Psychiatry Seoul National University Seoul Republic of Korea Department of Neuropsychiatry and Center for Sleep and Chronobiology Seoul National University Hospital Seoul Republic of Korea Department of Neuropsychiatry Seoul National University Hospital Seoul Republic of Korea Seoul National University Hospital Seoul Republic of Korea Department of Psychiatry Shiga University of Medical Science Otsu Japan Division of Public Health Department of Social Medicine Nihon University School of Medicine Tokyo Japan Department of Public Health Nihon University School of Medicine Tokyo Japan Division of Environmental and Preventive Medicine Department of Social Medicine Faculty of Medicine Tottori University Yonago Japan Department of Hygiene and Preventive Medicine Fukushima Medical University Fukushima Japan Department of Public Health Showa University School of Medicine Tokyo Japan Faculty of Humanities Seitoku University Matsudo Japan Section on Behavioral Science Division of Clinical Research National Hospital Organization Kurihama Alcoholism Center Yokosuka Japan Department of Otolaryngology Second Hospital Fujita Health University Nagoya Japan National Hospital Organization Kurihama Alcoholism Center Kanagawa Japan Metropolitan Police Department Criminal Investigation Laboratory Tokyo Japan Japan Somnology Center Neuropsychiatrie Research Institute Tokyo Japan Department of Somnology Tokyo Medical University Tokyo Japan Japan Somnology Center Neuropsychiatric Research Institute Tokyo Japan Department of Somnology Tokyo Medical and Dental University Tokyo Japan Center for Sleep and Chronobiology Department of Psychiatry and Behavioral Science Seoul National University College of Medicine Seoul Republic of Korea Department of Psychiatry and Behavioral Science Center for Sleep and Chronobiology Seoul Republic of Korea Department of Physiology Al

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