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

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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QuESt: a QoS-based energy efficient sensor routing protocol

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WIRELESS COMMUNICATIONS & MOBILE COMPUTING 2009年第3期9卷 417-426页

作者： Saxena, Navrati Roy, Abhishek Shin, Jitae Sungkyunkwan Univ Sch Informat & Commun Engn Suwon 440746 South Korea Samsung Elect WiBro Syst Lab Suwon South Korea

Rapid penetration of smart wireless devices and enormous growth of wireless communication technologies has already set the stage for deployment of wireless sensor networks (WSNs). While these small sensor nodes are often considered as the future of wireless communications, they also Suffer from energy constraints. On the other hand. with increasing demand for real-time services in next generation wireless networks, quality-of-service (QoS)-based routing has emerged as an interesting research topic. Naturally offering some QoS-guarantee in sensor networks raises significant challenges. The network needs to cope up with battery-constraints, while providing precise QoS (end-to-end delay and bandwidth requirement) guarantee. More precisely, designing such QoS-protocols, optimizing multiple objectives, is computationally intractable. Based on the multi-objective genetic algorithm (MOGA), in this paper we propose a QoS-based energy-efficient sensor rowing (QuESt) protocol that determines application-specific, near-optimal sensory-routes demand, by optimizing multiple QoS parameters, (end-to-end delay and bandwidth requirements) and energy consumption. Simulation results demonstrate that the proposed protocol is capable of discovering a set of QoS-based, near-optimal routes, even with imprecise network information. Copyright (C) 2007 John Wiley & Sons, Ltd.

关键词： sensor networks multi-objective genetic algorithm QoS routing

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Adaptive approximation in multi-objective optimization for full stochastic fatigue design problem

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MARINE STRUCTURES 2009年第3期22卷 610-632页

作者： Jang, Beom-Seon Ko, Dae-Eun Suh, Yong-Suk Yang, Young-Soon Samsung Heavy Ind Struct Res Part Marine Res Inst Geoje Si 656710 Gyeongsangnam D South Korea Dong Eui Univ Dept Naval Architecture & Ocean Engn Pusan 614714 South Korea Seoul Natl Univ Dept Naval Architecture & Ocean Engn Seoul 151742 South Korea

As the size of ship has grown rapidly, the importance of exact fatigue strength assessment has been recognized more and more. High concern about fatigue crack often raises target fatigue life to two or three times of ship lifetime. This leads to the use of very thick plates to reduce dynamic stress range or the application of weld toe grinding to reduce stress concentration or removing weld defects. However, such measures can cause some troubles in fabrication process. As a fatigue strength assessment procedure, full stochastic fatigue analysis based on wave loads analysis has been recommended due to its high accuracy and straightforward approach. However, its huge computing time hinders a ship designer from making iterative explorations for a better design to minimize the use of aforementioned measures. This paper proposes an efficient approach to optimize plate thicknesses around hot spots and the applications of weld toe grinding with meeting the required target fatigue life based on the full stochastic fatigue assessment. Two conflicting objectives are taken into consideration: to minimize steel weight and to minimize total weld toe grinding length. Whether to employ weld toe grinding or not for a hot spot can be seen as a selection variable. In order to treat such selection variables along with continuous variables in the multi-objective optimization, multi-objective genetic algorithm (MOGA) is introduced. This paper also employs adaptive approximation framework to resolve the computational burden of the full stochastic fatigue analysis in the optimization. The strategy to refit approximations iteratively can minimize the required number of analysis. A convergence criterion of the adaptive approximation framework is newly proposed considering the feature of discrete objective function attributed to the introduction of selection variables. One of the objective functions, toe grinding length, is purely depending on how many hot spots toe grindings are applied to.

关键词： Adaptive approximation Full stochastic fatigue analysis Liquid dome opening multi-objective genetic algorithm

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Stochastic multi-objective production-distribution network design using simulation-based optimization

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2009年第2期47卷 479-505页

作者： Ding, Hongwei Benyoucef, Lyes Xie, Xiaolan ISGMP COSTEAM Project INRIA Metz France IBM China Res Lab Beijing Peoples R China Ctr Hlth Sci & Engn St Etienne France

This paper addresses the design of production-distribution networks including both supply chain configuration and related operational decisions such as order splitting, transportation allocation and inventory control. The goal is to achieve the best compromise between cost and customer service level. An optimization methodology that combines a multi-objective genetic algorithm (MOGA) and simulation is proposed to optimize not only the structure of the production-distribution network but also its operation strategies and related control parameters. A flexible simulation framework is developed to enable the automatic simulation of the production-distribution network with all possible configurations and all possible control strategies. To illustrate its effectiveness, the proposed method is applied to a real life case study from automotive industry.

关键词： Production-distribution network design Supply chains multi-objective genetic algorithm Simulation-based optimization

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LDA Pre-processing for Classification: Class-Dependent Single objective GA and multi-objective GA Approaches

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10th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2009)

作者： Khalidji, Modjtaba Moeinzadeh, Hossein Akbari, Ahmad Raahemi, Bijan Iran Univ Sci & Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9783642043932

Pre-processing of classification data can be helpful regardless of the type of classifier. The objective of this pre-processing step is to achieve a high degree of separation among classes before the classifier is trained or tested. This results into a trace ratio problem which is difficult to solve. Methods such as Linear Discriminant Analysis (LDA) have already been used for the solution of this problem by turning it into a simpler yet inexact problem. Also, in classical LDA, the covariances of different classes are assumed to be similar, which is not the case in real-world problems. In this paper, a class-dependent approach to finding the linear transformation is proposed. This method solves the trace ratio problem directly and also removes the requirement of similar covariance matrices. While giving good results, the method is computationally expensive. To reduce the computational cost while maintaining the benefits of the classdependent method, a multi-objective formulation is proposed and solved using NSGA-II. Simulation results show great improvement in classification using various classifiers.

关键词： Pre-processing Classification Linear Discriminate Analysis genetic algorithm multi-objective genetic algorithm

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A Study on TODO Task Management at Home

A Study on TODO Task Management at Home

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International Conference of Soft Computing and Pattern Recognition

作者： Ajikata, Sayaka Kobayashi, Ichiro Ochanomizu Univ Grad Sch Humanities & Sci Kobayashi Lab Adv SciBunkyo Ku 2-1-1 Ohtsuka Tokyo 1128610 Japan Ochanomizu Univ Grad Sch Humanities & Sci Adv Sci Bunkyo Ku Tokyo 1128610 Japan

ISBN: (纸本)9781424453306

In the small community like a family, there exist several TODO tasks to be performed cooperatively by the members for making the community life easier. The TODO tasks have to be performed by someone in the community, therefore, it is preferable that the tasks should be done by the members without a big burden. In this context, we focus on TODO task management in a family, and propose a method to make a schedule in which the family members cooperate each other to achieve TODO tasks by taking account of multiple constraints, e.g., members' expert ability, schedules, etc. To make such a schedule, we use multi-objective genetic algorithm.

关键词： multi-objective genetic algorithm TODO task assignment family

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Stochastic multi-objective production-distribution network design using simulation-based optimization

Stochastic multi-objective production-distribution network d...

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12th IFAC/IFIP/IFORS/IEEE Symposium on Information Control Problems in Manufacturing (INCOM 2006)

作者： Ding, Hongwei Benyoucef, Lyes Xie, Xiaolan ISGMP COSTEAM Project INRIA Metz France IBM China Res Lab Beijing Peoples R China Ctr Hlth Sci & Engn St Etienne France

关键词： Production-distribution network design Supply chains multi-objective genetic algorithm Simulation-based optimization

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MOEA-Based Fuzzy Control for Seismically Excited Structures

MOEA-Based Fuzzy Control for Seismically Excited Structures

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4th International Symposium on Intelligence Computation and Applications

作者： Ning, Xiang-liang Tan, Ping Zhou, Fu-lin Harbin Inst Technol Shenzhen Grad Sch Shenzhen 518055 Peoples R China Guangdong Key Lab Earthquake Engn & Appl Tech Guangzhou 510405 Guangdong Peoples R China

ISBN: (纸本)9783642049613

To guarantee the safety and functionality of structures simultaneously at different levels of seismic loadings, this paper proposes a multi-objective switching fuzzy control (MOSFC) strategy. MOSFC functions as a trigger with two control states considered. When the structure is at the state of linear, the main objection of control is the peak acceleration. On the other hand, once the nonlinear appears, the control of peak inter-storey drift is the main objection. multi-objective genetic algorithm, NSGA-II, is employed for optimizing the fuzzy control rules. A scaled model of a six-storey building with two MR dampers installed at the two bottom floors is simulated here. Linear and Nonlinear numerical simulations demonstrate the effectiveness and robustness.

关键词： multi-objective genetic algorithm fuzzy control robustness

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