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A HYBRIDIZED NEURO-genetic SOLUTION FOR CONTROLLING INDUSTRIAL R³ WORKSPACE

NEURAL NETWORK WORLD 2010年第7期20卷 811-824页

作者： Irigoyen, E. Larrea, M. Valera, J. Gomez, V. Artaza, F. Univ Basque Country UPV EHU Dept Syst Engn & Automat Control Computat Intelligence Grp ETSI Bilbao 48013 Spain

This work presents a hybridized neuro-genetic control solution for R-3 workspace application. The solution is based on a multi-objective genetic algorithm reference generator and an adaptive predictive neural network strategy. The trajectory calculation between two points in an R-3 workspace is a complex optimization problem considering the fact that there are multiple objectives, restrictions and constraint functions which can play an important role in the problem and be in competition. We solve this problem using genetic algorithms, in a multi objective optimization strategy. Subsequently, we enhance a training algorithm in order to achieve the best adaptation of the neural network parameters in the controller which is responsible for generating the control action for a nonlinear system. As an application of the proposed hybridized control scheme, a crane tracking control is presented.

关键词： Hybrid neuro-genetic solution optimal trajectory generation multi-objective genetic algorithm nonlinear neural control adaptive predictive control

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The behavior of MENA oil and non-oil producing countries in international portfolio optimization

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QUARTERLY REVIEW OF ECONOMICS AND FINANCE 2010年第4期50卷 415-423页

作者： Mansourfar, Gholamreza Mohamad, Shamsher Hassan, Taufiq Urmia Univ Fac Econ & Management Dept Accounting & Finance Orumiyeh Iran Univ Putra Malaysia Grad Sch Management Serdang Malaysia Univ Putra Malaysia Dept Accounting & Finance Fac Econ & Management Serdang Malaysia

It is well documented in developed economies that portfolio investment across national borders brings benefits of increasing returns and/or reducing risk. Dividing MENA stock markets into two main groups (oil producing and non-oil producing countries), this study examines the potential role of each group in providing diversification benefits for international investors. In addition, the behavior of the long and the short-run Efficient Frontiers (EFs) constructed by each of the sub-groups and the combined MENA markets is explored. multi-objective international portfolio models are proposed under Mean-Variance and Mean-Lower Partial Moment frameworks, and the multiple Fitness Function genetic algorithm (MFFGA) is used to find the EFs of optimal portfolios. The findings indicate that the stock markets of oil producing countries can be considered as a potential avenue for international portfolio diversification for investors not only from the same countries but also from the other MENA markets. It was also found that international portfolios constructed from the combination of MENA equity markets are more stable compared to the portfolios of sub-group markets. Further, the findings indicate that the behavior of short-term EFs in the MENA region cannot be predicted by the behavior of long-term EFs. (C) 2010 The Board of Trustees of the University of Illinois. Published by Elsevier B.V. All rights reserved.

关键词： International portfolio optimization multi-objective genetic algorithm Lower Partial Moment MENA Emerging markets

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IPM motor rotor design by means of FEA-based multi-objective optimization

IPM motor rotor design by means of FEA-based multi-objective...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Pellegrino, G. Cupertino, F. Politecn Torino Cso Duca degli Abruzzi 24 I-10129 Turin Italy Politecn Bari I-70125 Bari Italy

ISBN: (纸本)9781424463916

The design optimization of IPM motors for wide speed ranges is pursued by means of a FEA-based multi-objective genetic algorithm (MOGA). Respect to previous works in the literature, the proposed approach evaluates the motor performance with a very limited number of simulations, making FEA optimization more attractive. The 3 goal functions (motor torque, torque ripple and flux weakening capability) are evaluated by means of 7 static FEA runs, that means nearly 20 seconds per tentative motor with a laptop computer. The paper is focused on the rotor design, since it is the most controversial part of IPM design and the most difficult to be modeled due to magnetic saturation. Three different approaches are presented: a fast one, based on 2-objective optimization, a hybrid one, based on 2-objective optimization and 3-objective refinement, and actual 3-objective optimization. The results presented here will be the base for future, more comprehensive optimization.

关键词： FEA IPM motor rotor design finite element analysis flux weakening capability genetic algorithms magnetic saturation motor torque multi-objective genetic algorithm permanent magnet motors rotors torque ripple wide speed ranges

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A New Hybrid Optimization of Reactive Optimization and Voltage Stability

A New Hybrid Optimization of Reactive Optimization and Volta...

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Asia-Pacific Power and Energy Engineering Conference (APPEEC)

作者： Zhou Ling Hao Fuzhong Zhao Feng Tan Jie Wang Dongping Wang Aiqin Hou Lifeng HoHai Univ Sch Elect Engn Nanjing Jiangsu Peoples R China Jiyuan Power Supply Bur Jiyuan Peoples R China

ISBN: (纸本)9781424448135

improving voltage quality and reducing power loss were the main goals of power system reactive optimization. The traditional method could reach the best economic benefit by optimizing the power flow, when the margin of the power system was enough. Nowadays, the safety grows more and more important because of the development of power system. Static voltage stability margin is an important index of power system safety. A new hybrid optimization had been put forward in this paper. The goal of this method was giving consideration to reducing power loss and increasing static voltage stability margin. At last, a simulation of IEEE-30 was made in this paper. The result of simulation showed this method is very effective.

关键词： voltage stability reactive optimization multi-objective genetic algorithm

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multi-objective genetic algorithms for Trajectory Optimization of Space Manipulator

Multi-Objective Genetic Algorithms for Trajectory Optimizati...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Zhengxiong Liu Panfeng Huang Jie Yan Gang Liu Research Center for Intelligent Robotics College of Astronautics Northwestern Polytechnical University Infineon Technologies(Xi'an)Ltd

ISBN: (纸本)9781424427994

This paper propose a multi-objective optimization algorithm to optimize the motion path of space manipulator with multi-objective function. In this formulation, multi-objective genetic algorithm (MOGA) is used to minimize the multi-objective function. The planning procedure is performed in joint space and with respect to all constraints, such as joint angle constraints, joint velocity constraints, torque constraints. We use a MOGA to search the optimal joint inter-knot parameters in order to realize the optimal motion trajectory for space manipulator. These joint inter-knot parameters mainly include joint angle and joint angular velocities. The simulation results test that the proposed multi-objective genetic algorithm has satisfactory performance.

关键词： Space manipulator multi-objective genetic algorithm Optimization

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CONSTRUCTION OF INTERPRETABLE AND PRECISE FUZZY MODELS USING FUZZY CLUSTERING AND multi-objective genetic algorithm

CONSTRUCTION OF INTERPRETABLE AND PRECISE FUZZY MODELS USING...

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2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

作者： YUAN-LONG HOU ZONG-YI XING QIANG GAO YONG ZHANG LI-MIN JIA School of Mechanical Engineering Nanjing University of Science and Technology Jiangsu 210094 Chi School of Transportation Beijing Jiaotong University Beijing 100044 China

An approach to construct interpretable and precise fuzzy models from data is proposed. Interpretability, which is one of the most important features of fuzzy models, is analyzed *** a modified fuzzy clustering algorithm, combined with the least square method, is used to identify the initial fuzzy model. Third, the multi-objective genetic algorithm and interpretability-driven simplification techniques are proposed to evolve the initial fuzzy model to optimize its structure and parameters iteratively, thus interpretability and precision of the fuzzy model are improved. Finally, the proposed approach is applied to the Mackey-Glass tine series, and the results show its validity.

关键词： Fuzzy modeling Fuzzy clustering multi-objective genetic algorithm TS fuzzy model Interpretability

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The performance analysis of a multi-objective immune genetic algorithm for flexible job shop scheduling

The performance analysis of a multi-objective immune genetic...

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PROLAMAT 2006 Conference on Knowledge Enterprise

作者： Wu, XiuLi Sun, ShuDong Niu, GangGang Zhai, YinNi Northwestern Polytech Univ Dept Mech & Elect Engn Xian 710072 Peoples R China

ISBN: (纸本)0387344020

First, a multi-objective immune genetic algorithm integrating immune algorithm and genetic algorithm for flexible job shop scheduling is designed. Second, Markov chain is used to analyze quantitatively its convergence. Third, a simulation experiment of the flexible job shop scheduling is carried out. Running results show that the proposed algorithm can converge to the Pareto frontier quickly and distribute evenly along the Pareto frontier.

关键词： flexible job shop scheduling multi-objective genetic algorithm convergence Markov chain

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Process parameters optimization of sheet metal forming in drawing process based on multi-objective genetic algorithm

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Zhongguo Jixie Gongcheng/China Mechanical Engineering 2006年第SUPPL.期17卷 74-76页

作者： Pan, Jiangfeng Zhong, Yuexian Yuan, Chaolong Key Lab. for AMMPT Tsinghua University Beijing 100084 China

An optimization model was established with MOGA and numerical simulation, in which several process parameters (e. g. blank-holding force, friction coefficient) were optimization variables, and several forming problems (e.g. crinkling, cracking) were optimization objections. An ANN was built to connect process parameters and simulation results which improved the optimization efficiency. At last a model of the exhaust muffler was provided. The result shows that the forming problems can be avoided via numerical simulation. It indicates that the algorithm has good optimization effect.

关键词： Artificial neural network multi-objective genetic algorithm Sheet drawing

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genetic algorithm based search on the role of variables in the work hardening process of multiphase steels

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COMPUTATIONAL MATERIALS SCIENCE 2009年第1期45卷 158-166页

作者： Ganguly, S. Datta, S. Chakraborti, N. Bengal Engn & Sci Univ Sch Mat Sci & Engn Sibpur 711103 Howrah India Indian Inst Technol Dept Met & Mat Engn Kharagpur 721302 W Bengal India

multi-objective optimizations of strength and ductility of multiphase steels are conducted using genetic algorithms (GAs), to investigate the role of the composition and process variables in their complicated work hardening process. Neural network based computational models, describing the complex correlations between the decision parameters for processing and materials chemistry of such steels, are developed using existing data and are used for the fitness functions. The cases of both high strength low alloyed steels (HSLA) and the transformation induced plasticity (TRIP) aided steel are separately studied and the findings are compared and contrasted. The Pareto solutions are used Successfully to study the role of the parameters at different combination of strength and ductility. The findings are also utilized for qualitative assessment of the dominant mechanisms behind the work hardening of the steels. (C) 2008 Elsevier B.V. All rights reserved.

关键词： multiphase steel TRIP Strength Ductility multi-objective genetic algorithm Work hardening

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On multi-objective optimization of geometry of staggered herringbone micromixer

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MICROFLUIDICS AND NANOFLUIDICS 2009年第1期7卷 29-43页

作者： Cortes-Quiroz, Cesar Augusto Zangeneh, Mehrdad Goto, Akira UCL Dept Mech Engn London England Ebara Res Co Ltd Fujisawa Kanagawa Japan

A design methodology for micromixers is presented which systematically integrates computational fluid dynamics (CFD) with an optimization methodology based on the use of design of experiments (DOE), function approximation technique (FA) and multi-objective genetic algorithm (MOGA). The methodology allows the simultaneous investigation of the effect of geometric parameters on the mixing performance of micromixers whose design strategy is based fundamentally on the generation of chaotic advection. The methodology has been applied on a Staggered Herringbone Micromixer (SHM) at several Reynolds numbers. The geometric features of the SHM are optimized and their effects on mixing are evaluated. The degree of mixing and the pressure drop are the performance criteria to define the efficiency of the micromixer for different design requirements.

关键词： Design of experiments multi-objective genetic algorithm multi-objective optimization Response surface Staggered Herringbone Micromixer

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