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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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multi-objective optimization of sheet metal forming process using Pareto-based genetic algorithm

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JOURNAL OF MATERIALS PROCESSING TECHNOLOGY 2008年第1-3期208卷 499-506页

作者： Liu Wei Yang Yuying Harbin Inst Technol Sch Mat Sci & Engn Harbin 150001 Peoples R China

Finite element analysis and optimization technique have been integrated to solve the optimal process parameters of sheet metal forming by transforming multi-objective problem into a single-objective problem in many studies. In this paper, a Pareto-based multi-objective genetic algorithm was applied to optimize sheet metal forming process. in the proposed optimal model, blank-holding force and draw-bead restraining force were optimized as design variables in order to make objective functions of fracture, wrinkle, insufficient stretching and thickness varying minimized simultaneously. The steps of optimization procedure include the using of Latin hypercube design for sample producing, response surface model for coarse fitting and incremental finite element analysis program for exact solving. An auto-body panel stamping case shows that this approach is more effective and accurate than traditional finite element analysis method and the 'trial and error' procedure. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Sheet metal forming Finite element analysis Latin hypercube design Response surface model multi-objective genetic algorithm

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A Robust Image Watermarking Method in Wavelet Domain Using genetic algorithm

A Robust Image Watermarking Method in Wavelet Domain Using G...

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4th International Conference on Availability, Reliability and Security

作者： Amiri, S. Hamid Jamzad, Mansour Sharif Univ Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9781424435722

Robustness against attacks is an important requirement in image watermarking. This paper presents a robust watermarking algorithm in wavelet transform domain. Firstly, original image is decomposed into its subbands using three level wavelet transform, then, significant coefficients with the same position in HL, LH and HH subbands of the last level are extracted to make a triplet. To embed a watermark bit into a triplet, the standard deviation of triplet coefficients magnitude is set to zero for zero bit or increased for a one bit. Three constant are used to increase the standard deviations of a triplet coefficients. The value of these constants affects the robustness of algorithm and the quality of image. To specify the optimal values for constants a multi-objective genetic algorithm (MOGA) is used. Experimental results reveal the high robustness of the proposed algorithm against common image processing attacks (blurring, median filter, sharpening, noise addition, ect), and print and scan distortion.

关键词： Attacks Fidelity multi-objective genetic algorithm Print and Scan Distortions Robustness Watermarking Wavelet Transform

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An Application and Characteristic Analysis of MOGA for Bi-objective Optimal Component Allocation Problem in Series-Parallel Redundant System

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ELECTRONICS AND COMMUNICATIONS IN JAPAN 2009年第9期92卷 7-16页

作者： Yamachi, Hidemi Tsujimura, Yasuhiro Yamamoto, Hisashi Kambayashi, Yasushi Nippon Inst Technol Miyashiro Saitama Japan Tokyo Metropolitan Univ Tokyo Japan

We discuss a solution method based on evolutionary technology for the optimal component allocation problem in a series-parallel redundant system. A series-parallel system consists of subsystems that are connected in series and each subsystem consists of interchangeable components in parallel. There are some heuristic methods to approximately solve the optimal component allocation problem for series-parallel systems. We have formulated this problem as a multi-objective optimization problem minimizing the system cost and maximizing the system reliability, and proposed an algorithm that obtains the exact solutions (Pareto solutions) of the problems in an efficient way. Because this problem is one of the NP-complete problems, it is difficult to obtain the optimal solution for the large-scale problems and methods that obtain the exact solutions are not known. The algorithm utilizes the depth-first search method to eliminate useless searches and employs the branch-and-bound method to obtain the Pareto solutions. According to the results of our numerical experiments, the algorithm searches the Pareto solutions in practical execution time for not-so-large-scale problems. In order to solve larger-scale problems, we propose a multi-objective genetic algorithm (MOGA). We evaluate the ability of the MOGA by comparison with the exact solution method by using various scale problems. Through those experiments, we discuss the characteristics of this problem and analyze the effectiveness of our method. (C) 2009 Wiley Periodicals, Inc. Electron Comm Jpn, 92(9): 7-16, 2009;Published online in Wiley InterScience (***). DOI 10.1002/ecj.10100

关键词： series-parallel redundant system optimal allocation problem multi-objective optimization multi-objective genetic algorithm

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