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第二十四届中国控制会议

作者： Ma Qingliang, Hu Changhua (Teaching and Research Unit 302, Xi'an Research Institute of Hi-tech, Xi'an 710025, China)

A novel approach to H/Hoptimal control is presented based on multi-objective genetic algorithm （MOGA）. To design H/Hcontroller with less conservativeness, a kind of MOGA for H/Hcontrol （HHMOGA）is especially developed. HHMOGA takes the solutions of linear matrix inequality （LMI） method as initial population. Non-dominated sorting, niche, and elitist strategy are employed in order to ensure a better design. Simulation results show that HHMOGA can achieve better performances as compared with LMI method.

关键词： optimal control H2/H_∞ control multi-objective genetic algorithm

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Search for phosphors for use in displays and lighting using heuristics-based combinatorial materials science

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JOURNAL OF THE SOCIETY FOR INFORMATION DISPLAY 2009年第12期17卷 1073-1080页

作者： Sharma, Asish Kumar Sohn, Kee-Sun Sunchon Natl Univ Dept Mat Sci & Met Engn Sunchon City 540742 Chonnam South Korea

According to the recent demand for materials for use in various displays and solid-state lighting, new phosphors with improved performance have been consistently pursued. multi-objective genetic-algorithm-assisted combinatorial-material-search (MOGACMS) strategies have been applied to various multi-compositional inorganic systems to search for new phosphors and to optimize the properties of phosphors. In addition, the troublesome, complex problem of high-throughput experimentation (HTE), the inconsistency, which is frequently faced by combinatorial material scientists, is especially emphasized. The luminance and inconsistency was treated as two objective functions in our MOGACMS strategy to pinpoint and optimize promising phosphors with high photoluminance and reliable reproducibility. Using MOGACMS, several multi-dimensional oxide systems were screened in term of the minimization of inconsistency and the maximization of luminance.

关键词： Combinatorial material science multi-objective genetic algorithm PDP red phosphor

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U-shaped assembly line balancing problem with genetic algorithm

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2008年第16期46卷 4637-4649页

作者： Hwang, Rea Kook Katayama, Hiroshi Gen, Mitsuo Waseda Univ Grad Sch Sci & Engn Shinjuku Ward Tokyo 1690072 Japan Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Fukuoka 8080135 Japan

This paper presents a multi-objective genetic algorithm (moGA) to solve the U-shaped assembly line balancing problem (UALBP). As a consequence of introducing the just-in-time (JIT) production principle, it has been recognized that U-shaped assembly line systems offer several benefits over the traditional straight line systems. We consider both the traditional straight line system and the U-shaped assembly line system, thus as an unbiased examination of line efficiency. The performance criteria considered are the number of workstations (the line efficiency) and the variation of workload. The results of experiments show that the proposed model produced as good or even better line efficiency of workstation integration and improved the variation of workload.

关键词： U-shaped assembly line multi-objective genetic algorithm priority-based coding

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GA-optimized fuzzy logic control of a large-scale building for seismic loads

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ENGINEERING STRUCTURES 2008年第2期30卷 436-449页

作者： Shook, David A. Roschke, Paul N. Lin, Pei-Yang Loh, Chin-Hsiung Texas A&M Univ Zachry Dept Civil Engn College Stn TX 77843 USA Natl Ctr Res Earthquake Engn Taipei Taiwan Natl Taiwan Univ Dept Civil Engn Taipei 106 Taiwan

Validation of fuzzy logic controllers that are optimized by a genetic algorithm is pursued in this study. Fuzzy logic controllers are designed to manage two 20 kN magnetorheological dampers for mitigation of seismic loads applied to a 9 m tall, three-story steel frame benchmark building. In order to develop a set of robust controllers that are sensitive to a variety of excitations, a genetic algorithm that considers multiple objectives concurrently is proposed. Four optimization objectives have been selected which necessitates employment of a controlled elitist genetic algorithm. Optimal controllers are identified and validated through numerical simulation and full-scale experimental shake table tests for a variety of seismic excitations. Furthermore, a modified version of the same genetic algorithm is used to identify a state-space representation of the benchmark structure. Results show that optimized fuzzy logic controllers are robust and effective in reduction of both displacement and acceleration responses for both near- and far-field seismic events. (c) 2007 Elsevier Ltd. All rights reserved.

关键词： Experimental testing Magnetorheological damper multi-objective genetic algorithm Fuzzy logic control System identification

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Solving a multi-objective no-wait flow shop scheduling problem with an immune algorithm

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2008年第9-10期36卷 969-981页

作者： Tavakkoli-Moghaddam, R. Rahimi-Vahed, A. R. Mirzaei, A. H. Univ Tehran Fac Engn Dept Ind Engn Tehran Iran Tarbiat Modares Univ Fac Engn Dept Ind Engn Tehran Iran

Flow shop scheduling problems have gained wide attention both in practical and academic fields. In this paper, we consider a multi-objective no-wait flow shop scheduling problem by minimizing the weighted mean completion time and weighted mean tardiness simultaneously. Since a flow shop scheduling problem has been proved to be NP-hard in a strong sense, an effective immune algorithm (IA) is proposed for searching locally the Pareto-optimal frontier for the given problem. To validate the performance of the proposed algorithm in terms of solution quality and diversity level, various test problems are carried out and the efficiency of the proposed algorithm, based on some comparison metrics, is compared with a prominent multi-objective genetic algorithm, i.e., strength Pareto evolutionary algorithm II (SPEA-II). The computational results show that the proposed IA outperforms the above genetic algorithm, especially for large problems.

关键词： multi-objective flow shop scheduling no wait immune algorithm multi-objective genetic algorithm

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Solving a bi-criteria permutation flow-shop problem using shuffled frog-leaping algorithm

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SOFT COMPUTING 2008年第5期12卷 435-452页

作者： Rahimi-Vahed, Alireza Mirzaei, Ali Hossein Tarbiat Modares Univ Dept Ind Engn Fac Engn Tehran Iran

Flow shop problems as a typical manufacturing challenge have gained wide attention in academic fields. In this paper, we consider a bi-criteria permutation flow shop scheduling problem, where the weighted mean completion time and the weighted mean tardiness are to be minimized simultaneously. Due to the complexity of the problem, it is very difficult to obtain optimum solution for this kind of problems by means of traditional approaches. Therefore, a new multi-objective shuffled frog-leaping algorithm (MOSFLA) is introduced for the first time to search locally Pareto-optimal frontier for the given problem. To prove the efficiency of the proposed algorithm, various test problems are solved and the reliability of the proposed algorithm, based on some comparison metrics, is compared with three distinguished multi-objective genetic algorithms, i.e. PS-NC GA, NSGA-II, and SPEA-II. The computational results show that the proposed MOSFLA performs better than the above genetic algorithms, especially for the large-sized problems.

关键词： bi-criteria permutation flow shop scheduling multi-objective shuffled frog-leaping algorithm multi-objective genetic algorithm

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multi-objective genetic algorithm for the automated planning of a wireless sensor network to monitor a critical facility

Multi-objective genetic algorithm for the automated planning...

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Conference on Sensors and Command, Control, Communications and Intelligence (C31) Technologies for Homeland Security and Homeland Defense III

作者： Jourdan, DB de Weck, OL MIT Dept Aeronaut & Astronaut Cambridge MA 02139 USA

ISBN: (纸本)0819453269

This paper examines the optimal placement of nodes for a Wireless Sensor Network (WSN) designed to monitor a critical facility in a hostile region. The sensors are dropped from an aircraft, and they must be connected (directly or via hops) to a High Energy Communication Node (HECN), which serves as a relay from the ground to a satellite or a high-altitude aircraft. The sensors are assumed to have fixed communication and sensing ranges. The facility is modeled as circular and served by two roads. This simple model is used to benchmark the performance of the optimizer (a multi-objective genetic algorithm, or MOGA) in creating WSN designs that provide clear assessments of movements in and out of the facility, while minimizing both the likelihood of sensors being discovered and the number of sensors to be dropped. The algorithm is also tested on two other scenarios;in the first one the WSN must detect movements in and out of a circular area, and in the second one it must cover uniformly a square region. The MOGA is shown again to perform well on those scenarios, which shows its flexibility and possible application to more complex mission scenarios with multiple and diverse targets of observation.

关键词： multi-objective genetic algorithm wireless sensor network network planning node placement facility monitor coverage

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Two storage inventory problem with dynamic demand and interval valued lead-time over finite time horizon under inflation and time-value of money

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2008年第1期185卷 170-194页

作者： Dey, Jayanta Kumar Mondal, Shyamal Kumar Maiti, Manoranjan Mahishadal Raj Coll Dept Math Mahishadal 721628 E Midnapore India Vidyasagar Univ Dept Appl Math Oceanol & Comp Programming Midnapore 721102 India

A finite time horizon inventory problem for a deteriorating item having two separate warehouses, one is a own warehouse (OW) of finite dimension and other a rented warehouse (RW), is developed with interval-valued lead-time under inflation and time value of money. Due to different preserving facilities and storage environment, inventory holding cost is considered to be different in different warehouses. The demand rate of item is increasing with time at a decreasing rate. Shortages are allowed in each cycle and backlogged them partially. Shortages may or may not be allowed in the last cycle and under this circumstance, there may be three different types of model. Here it is assumed that the replenishment cycle lengths are of equal length and the stocks of RW are transported to OW in continuous release pattern. For each model, different scenarios are depicted depending upon the re-order point for the next lot. Representing the lead-time by an interval number and using the interval arithmetic, the single objective function for profit is changed to corresponding multi-objective functions. These functions are maximized and solved by Fast and Elitist multi-objective genetic algorithm (FEMGA). The models are illustrated numerically and the results are presented in tabular form. (c) 2007 Elsevier B.V. All rights reserved.

关键词： interval valued lead-time interval numbers multi-objective inventory model two warehouse inflation and time value of money finite time horizon multi-objective genetic algorithm

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INITIAL-VALUED FIRST-ORDER FUZZY DIFFERENTIAL EQUATION IN BI-LEVEL INVENTORY MODEL WITH FUZZY DEMAND

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MATHEMATICAL MODELLING AND ANALYSIS 2008年第4期13卷 493-512页

作者： Das, B. Mahapatra, N. K. Maiti, M. Jhargram Raj Coll Dept Math Jhargram W Bengal India Panskura Banamali Coll Dept Math Panskura W Bengal India

In the present paper;two methods for the solution of an initial valued first ordered fuzzy differential equation are presented and applied in a fuzzy EOQ model. The constructed model is a bi-level inventory problem involving wholesaler-retailers-customers. The wholesaler buys and sells the item instantaneously to several retailers. In the next level, the retailers sell the units to customers with a time dependent imprecise demand;which introduce the fuzzy nature in the differential equation. The selling price of the item is a step-wise time dependent decreasing function. The fuzzy objectives are transformed into crisp one following fuzzy extension principle and centroid formula. The model is illustrated through Interactive Fuzzy Decision Making (IFDM) and multi objective genetic algorithm (MOGA) and the results from two methods are compared.

关键词： Fuzzy differential equation Bi-level inventory Newsboy problem Interactive fuzzy decision making method multi-objective genetic algorithm

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Application of smart system to textile industry: Preliminary design of a smart hanger for garment inspection

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JOURNAL OF THE TEXTILE INSTITUTE 2008年第6期99卷 569-580页

作者： Yuen, C. W. M. Fung, E. H. K. Wong, W. K. Hau, L. C. Chan, L. K. Hong Kong Polytech Univ Dept Mech Engn Kowloon Hong Kong Peoples R China Hong Kong Polytech Univ Inst Text & Clothing Kowloon Hong Kong Peoples R China

A smart inspection system, comprising three components, namely a smart hanger, a stitching-workmanship defect classification unit as well as a shade variation detection unit, is introduced for the textile industry. The preliminary design and optimization results of the smart hanger which stretches garments to provide enough tension to facilitate the inspection are presented. The hanger, with a Proportional and Derivative (PD) controller, is modeled as a four-link robot consisting of three revolute joints and one prismatic joint. The design objectives include (1) maximizing the link portions within effective regions at the final state, (2) minimizing the force or torque required to finish the stretching process, and (3) minimizing the settling time of the stretching process. In addition to control gains, the lengths of the four links as well as the desired movements of the joints are taken as design variables. The required transient behavior of the hanger is defined by the constraints on settling time and maximum overshoot. In order to prevent the clothes from being damaged by the links during the stretching process, geometrical constraints are imposed on the motions of the links. Optimization results, obtained by using the multi-objective genetic algorithm (MOGA), are presented in the form of Pareto solutions. After analysis, optimal parameters are selected and computer simulations are performed to investigate the transient behavior of the hanger. Satisfactory results are obtained, which provide a good foundation for the on going development of the smart inspection system.

关键词： Smart hanger design optimization multi-objective genetic algorithm garment inspection textile industry

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